##ITU_Thesis_Cort_ASQ ### Results of cortisol and ASQ total #28.07.2021 ### Data preparation #### #setwd("C:/Users/User/Desktop/Internship/RScripts/Cortisol/Master_Thesis") #setwd("C:/Users/alici/Desktop/Git_Folder/ITU_cortisol_analyses/Master_Thesis") ##libraries library(dplyr) library(gtsummary) library(gdata) library(VGAM) library(rcompanion) library(tidyverse) library(broom) library(apaTables) library(tidyr) library(data.table) require(MASS) library(APAstyler) ## 1. cortisol data #### load("Rdata/ITU_combined_cortisol_dates_times_wide_format.Rdata") comments_to_be_excluded <- c(88,6,12,3,15,9999,7,16,45,5,34,17,57,67,99) wide_cort <- wide_cort[!(wide_cort$notes %in% comments_to_be_excluded),] cort_IDs <- unique(wide_cort$participantID) wide_cort <- subset(wide_cort, CAR_time_interval < 0.75) final_cort <- wide_cort %>% tidyr::pivot_wider( id_cols = c(participantID), names_from = c(pregstage), # Can accommodate more variables, if needed. values_from = c(9,21:23) ) cors_CAR <- apa.cor.table(final_cort[,c("d_AUC_CAR_only_I","d_AUC_CAR_only_II","d_AUC_CAR_only_III")]) cors_DIUR <- apa.cor.table(final_cort[,c("cort_d_AUC_noCAR_I","cort_d_AUC_noCAR_II","cort_d_AUC_noCAR_III")]) cors_combined <- apa.cor.table(final_cort[,c("cort_d_AUC_I","cort_d_AUC_II","cort_d_AUC_III")]) load("Rdata/CAR_parameters.Rdata") CAR_parameters <- setDT(CAR_parameters, keep.rownames = "participantID_old") CAR_parameters <- separate(CAR_parameters, col = "participantID_old", into = c("participantID", NA), sep = "/I", extra = "drop") CAR_parameters <- CAR_parameters[,c("participantID", "hours_since_waking")] names(CAR_parameters)[2] <- "Morning_Slope" CAR_parameters <- subset(CAR_parameters, !(duplicated(CAR_parameters))) load("Rdata/Diurnal_parameters.Rdata") DIUR_parameters <- setDT(DIUR_parameters, keep.rownames = "participantID_old") DIUR_parameters <- separate(DIUR_parameters, col = "participantID_old", into = c("participantID", NA), sep = "/I", extra = "drop") DIUR_parameters <- DIUR_parameters[,c("participantID", "hours_since_waking")] names(DIUR_parameters)[2] <- "Diurnal_Slope" DIUR_parameters <- subset(DIUR_parameters, !(duplicated(DIUR_parameters))) ## add to cortisol dataframe final_cort <- left_join (final_cort, CAR_parameters) final_cort <- left_join (final_cort, DIUR_parameters) ## 2. maternal well-being during pregnancy #### load("Rdata/processed_wellbeingduringpreg_completevars.Rdata") #for now only sleep, anxiety and CESD are included cols <- c("1", "2",grep("gestage", names(q_data_complete)), grep("GWdiff_cort_qclosest", names(q_data_complete)), grep("Cesd", names(q_data_complete)), #grep("puqe", names(q_data_complete)), grep("PSQI", names(q_data_complete)), #grep("ESS", names(q_data_complete)), grep("BAI",names(q_data_complete))) q_data_sub <- q_data_complete[,c(as.numeric(cols))] q_data_sub <- subset(q_data_sub, abs(GWdiff_cort_qclosest_to_cortGW) < 2) ## Depressive Symptoms Severity q_data_sub$Depressive_Symptom_Severity <- NA q_data_sub$Depressive_Symptom_Severity_num <- NA for(i in 1:nrow(q_data_sub)){ m <- q_data_sub$Cesd_qclosest_to_cortGW_pregMean[[i]] if(!is.na(m)){ if(m<16){ q_data_sub$Depressive_Symptom_Severity[[i]] <- "Below_Clinical (CES-D < 16)" q_data_sub$Depressive_Symptom_Severity_num[[i]] <- 0 } if(m>=16){ q_data_sub$Depressive_Symptom_Severity[[i]] <- "Clinical (CES-D >= 16)" q_data_sub$Depressive_Symptom_Severity_num[[i]] <- 1 } } } q_data_sub$Depressive_Symptom_Severity <- factor(q_data_sub$Depressive_Symptom_Severity) #Anxiety Symptom Severity q_data_sub$Anxiety_Symptom_Severity <- NA q_data_sub$Anxiety_Symptom_Severity_num <- NA for(i in 1:nrow(q_data_sub)){ m <- q_data_sub$BAI_qclosest_to_cortGW_pregMean[[i]] if(!is.na(m)){ if(m>21){ q_data_sub$Anxiety_Symptom_Severity[[i]] <- "Moderate/Severe" q_data_sub$Anxiety_Symptom_Severity_num[[i]] <- 1 } if(m<=21){ q_data_sub$Anxiety_Symptom_Severity[[i]] <- "Low/Normal" q_data_sub$Anxiety_Symptom_Severity_num[[i]] <- 0 } } } q_data_sub$psych_distress <- rowSums(q_data_sub[,c("Depressive_Symptom_Severity_num", "Anxiety_Symptom_Severity_num")], na.rm=F) ### compute additive depression score q_data_sub$clinD <- NA for(i in 1:nrow(q_data_sub)){ cesd <- q_data_sub$Cesd_qclosest_to_cortGW[[i]] if(!is.na(cesd)){ if(cesd >= 16){ q_data_sub$clinD[[i]] <- 1 } if(cesd < 16){ q_data_sub$clinD[[i]] <- 0 } } } ##sum occasions of clinical depression together q_data_sub <- q_data_sub %>% group_by(participantID) %>% mutate(additive_clinD = sum(clinD, na.rm = T)) ## data selection q_data_subx <- q_data_sub[,c("participantID", "pregstage", "Cesd_qclosest_to_cortGW_pregMean", "Depressive_Symptom_Severity", "Depressive_Symptom_Severity_num", "BAI_qclosest_to_cortGW_pregMean", "PSQI_qclosest_to_cortGW_pregMean", "qclosest_based_clin_Cesd", "Cesd_qclosest_to_cortGW", "BAI_qclosest_to_cortGW", "PSQI_qclosest_to_cortGW", "additive_clinD")] ## Sample stratification ## those with multiple assessments at_least_two_assessments <- c() q_data_subx <- as.data.frame(q_data_subx) IDs <- unique(q_data_subx$participantID[!is.na(q_data_subx$Cesd_qclosest_to_cortGW)]) for(i in 1:length(IDs)){ ID <- IDs[[i]] #print(ID) trims <- unique(q_data_subx[q_data_subx$participantID == ID, c("pregstage")]) #print(length(trims)) if(length(trims) >= 2){ at_least_two_assessments <- append(at_least_two_assessments, ID) } } ### Data in wide format q_data_sub_final <- q_data_subx %>% tidyr::pivot_wider( id_cols = c(participantID, pregstage, Cesd_qclosest_to_cortGW_pregMean, Depressive_Symptom_Severity, Depressive_Symptom_Severity_num, BAI_qclosest_to_cortGW_pregMean, PSQI_qclosest_to_cortGW_pregMean, additive_clinD), names_from = c(pregstage), # Can accommodate more variables, if needed. values_from = c(8:11) ) # Qs transformations #### q_data_sub_final$Cesd_qclosest_to_cortGW_pregMean_cent <- c(scale(sqrt(q_data_sub_final$Cesd_qclosest_to_cortGW_pregMean), scale = TRUE)) q_data_sub_final$BAI_qclosest_to_cortGW_pregMean_cent <- c(scale(sqrt(q_data_sub_final$BAI_qclosest_to_cortGW_pregMean), scale = TRUE)) q_data_sub_final$PSQI_qclosest_to_cortGW_pregMean_cent <- c(scale(sqrt(q_data_sub_final$PSQI_qclosest_to_cortGW_pregMean), scale = TRUE)) #add categorical anxiety severity q_data_sub_final$Anxiety_Severity_greateroneSD_num <- ifelse(q_data_sub_final$BAI_qclosest_to_cortGW_pregMean_cent > 1, 1, 0) q_data_sub_final$Anxiety_Severity_greateroneSD <- factor(ifelse(q_data_sub_final$BAI_qclosest_to_cortGW_pregMean_cent > 1, "yes", "no")) q_data_sub_final$Anxiety_Severity_greateroneSD <- factor(q_data_sub_final$Anxiety_Severity_greateroneSD) #add psych distress score q_data_sub_final$psych_distress <- rowSums(q_data_sub_final[,c("Depressive_Symptom_Severity_num", "Anxiety_Severity_greateroneSD_num")], na.rm=F) q_data_sub_final$psych_distress <- factor(q_data_sub_final$psych_distress) q_data_sub_final$PSQI_severity <- NA for(i in 1:nrow(q_data_sub_final)){ psqi <- q_data_sub_final$PSQI_qclosest_to_cortGW_pregMean_cent[[i]] if(!is.na(psqi)){ q_data_sub_final$PSQI_severity[[i]] <- "mean" if(psqi <= -1){ q_data_sub_final$PSQI_severity[[i]] <- "-1SD" } if(psqi >= 1){ q_data_sub_final$PSQI_severity[[i]] <- "+1SD" } } } q_data_sub_final$PSQI_severity <- factor(q_data_sub_final$PSQI_severity) ## 3. maternal follow-up data #### followUp <- read.delim("Rdata/ITU_1to2YearsFollowup_MaternalandChildQuestionnaires.dat") names(followUp)[1] <- "participantID" relevant_followUp <- c("1", grep("CESD", names(followUp)), grep("BAI", names(followUp))) postpartum_followUp <- followUp[,c(as.numeric(relevant_followUp))] postpartum_followUp$ITU_1.7y_mother_CESD_sum_nomis <- as.numeric(gsub(",", ".", postpartum_followUp$ITU_1.7y_mother_CESD_sum_nomis)) postpartum_followUp$ITU_1.7y_mother_BAI_sum_no_missing <- as.numeric(gsub(",", ".", postpartum_followUp$ITU_1.7y_mother_BAI_sum_no_missing)) postpartum_df <- postpartum_followUp[,c("participantID", "ITU_1.7y_mother_CESD_sum_nomis", "ITU_1.7y_mother_BAI_sum_no_missing")] names(postpartum_df)[2] <- "postpartum_Cesd" names(postpartum_df)[3] <- "postpartum_BAI" ## Depressive Symptoms Severity postpartum_df$postpartum_Depressive_Symptom_Severity <- NA postpartum_df$postpartum_Depressive_Symptom_Severity_num <- NA for(i in 1:nrow(postpartum_df)){ m <- postpartum_df$postpartum_Cesd[[i]] if(!is.na(m)){ if(m<16){ postpartum_df$postpartum_Depressive_Symptom_Severity[[i]] <- "Non-Clinical (CES-D < 16)" postpartum_df$postpartum_Depressive_Symptom_Severity_num[[i]] <- 0 } if(m>=16){ postpartum_df$postpartum_Depressive_Symptom_Severity[[i]] <- "Clinical (CES-D >= 16)" postpartum_df$postpartum_Depressive_Symptom_Severity_num[[i]] <- 1 } } } postpartum_df$postpartum_Depressive_Symptom_Severity <- factor(postpartum_df$postpartum_Depressive_Symptom_Severity) #view(postpartum_df[,c("postpartum_Cesd","postpartum_Depressive_Symptom_Severity_num")]) #Anxiety Symptom Severity postpartum_df$postpartum_Anxiety_Symptom_Severity <- NA postpartum_df$postpartum_Anxiety_Symptom_Severity_num <- NA for(i in 1:nrow(postpartum_df)){ m <- postpartum_df$postpartum_BAI[[i]] if(!is.na(m)){ if(m>21){ postpartum_df$postpartum_Anxiety_Symptom_Severity[[i]] <- "Moderate/Severe" postpartum_df$postpartum_Anxiety_Symptom_Severity_num[[i]] <- 1 } if(m<=21){ postpartum_df$postpartum_Anxiety_Symptom_Severity[[i]] <- "Low/Normal" postpartum_df$postpartum_Anxiety_Symptom_Severity_num[[i]] <- 0 } } } #summary(factor(postpartum_df$postpartum_Anxiety_Symptom_Severity)) n = 5 #summary(factor(postpartum_df$postpartum_Depressive_Symptom_Severity)) n = 102 ### transformations postpartum_df$postpartum_BAI_cent <- c(scale(sqrt(postpartum_df$postpartum_BAI), scale = TRUE)) postpartum_df$postpartum_Cesd_cent <- c(scale(sqrt(postpartum_df$postpartum_Cesd), scale = TRUE)) ### anxiety severity by SD above the sample mean postpartum_df$postpartum_Anxiety_Severity_greateroneSD_num <- ifelse(postpartum_df$postpartum_BAI_cent > 1, 1, 0) postpartum_df$postpartum_Anxiety_Severity_greateroneSD <- factor(ifelse(postpartum_df$postpartum_BAI_cent > 1, "yes", "no")) postpartum_df$postpartum_psych_distress <- rowSums(postpartum_df[,c("postpartum_Depressive_Symptom_Severity_num", "postpartum_Anxiety_Severity_greateroneSD_num")], na.rm=F) postpartum_df$postpartum_psych_distress <- factor(postpartum_df$postpartum_psych_distress) postpartum_df_final <- postpartum_df[,c("participantID", "postpartum_BAI_cent", "postpartum_Cesd_cent", "postpartum_Depressive_Symptom_Severity", "postpartum_Anxiety_Severity_greateroneSD", "postpartum_psych_distress", "postpartum_Depressive_Symptom_Severity_num")] # 3.1 Maternal Education #### maternal_edu <- read.delim("Rdata/ITU maternal education.dat") names(maternal_edu)[1] <- "participantID" names(maternal_edu)[3] <- "Maternal_Education" maternal_edu <- maternal_edu[,c("participantID", "Maternal_Education")] library(naniar) maternal_edu <- maternal_edu %>% replace_with_na(replace = list(Maternal_Education = -9)) maternal_edu$Maternal_Education <- factor(maternal_edu$Maternal_Education, levels = c(1,2,3), labels = c("primary", "applied university", "university")) ## 4. Register data during pregnancy #### load("Rdata/processed_register_data.Rdata") register_data$Maternal_Smoking_During_Pregnancy <- factor(register_data$Maternal_Smoking_During_Pregnancy, levels = c("no", "quit_T1", "yes"), labels = c("no", "no", "yes")) register_data$Maternal_Hypertensive_Disorders_anyVSnone <- factor(register_data$Maternal_Hypertensive_Disorders_anyVSnone, levels = c(-999,0,1), labels = c("no", "no", "yes")) register_data$Maternal_Diabetes_Disorders_anyVSnone <- factor(register_data$Maternal_Diabetes_Disorders_anyVSnone, levels = c(-999,0,1), labels = c("no", "no", "yes")) register_data$Maternal_Body_Mass_Index_in_Early_Pregnancy_cent <- c(scale(register_data$Maternal_Body_Mass_Index_in_Early_Pregnancy, scale = F)) register_data$Weight_Gain_cent <- c(scale(register_data$Weight_Gain, scale = F)) register_data$Gestational_Age_Weeks_cent <- c(scale(register_data$Gestational_Age_Weeks, scale = F)) register_data$Child_Birth_Weight_cent <- c(scale(register_data$Child_Birth_Weight, scale = F)) register_data$Maternal_Age_Years_cent <- c(scale(register_data$Maternal_Age_Years, scale = F)) regis_final <- register_data[,c("participantID", "caseVScontrol", "Parity", "Maternal_Smoking_During_Pregnancy", "Maternal_Corticosteroid_Treatment_during_Pregnancy", "Maternal_Body_Mass_Index_in_Early_Pregnancy_cent", "Child_Sex" , "Gestational_Age_Weeks_cent", "Child_Birth_Weight_cent", "Weight_Gain_cent", "Maternal_Hypertensive_Disorders_anyVSnone", "Maternal_Diabetes_Disorders_anyVSnone", "Maternal_Age_Years_cent" )] ## 5. medication data #### medication <- read.delim("Rdata/ITU_psychotrophicmedication05July21_Maternal_CurrentPregnancy.dat") names(medication)[1] <- "participantID" ## 6. ASQ data #### ASQ <- read.csv2("Rdata/ASQ_dataset_ITU_01122020r_shorter.csv") names(ASQ)[1] <- "participantID" #extract only participants who have cortisol data and finalagerange scores #ASQ_final <- ASQ[ASQ$participantID %in% cort_IDs,c(1,3, 18:23)] ASQ_final <- ASQ[ASQ$participantID %in% cort_IDs,c(1,3, 18:23)] ASQ_final_samplesizes <- left_join(ASQ_final, wide_cort) ASQ_final$Child_ASQ_grossmotor_development_infancy_sum_finalagerange <- as.numeric(ASQ_final$Child_ASQ_grossmotor_development_infancy_sum_finalagerange) #normalized rank scores for(col in 3:ncol(ASQ_final)){ column <- ASQ_final[col] name <- paste0(names(ASQ_final[col]), "_norm") ASQ_final[name] <- blom(column, method = "rankit") } #dichotomize normalized rank scores for(col in 9:ncol(ASQ_final)){ column <- ASQ_final[col] old_name <- names(ASQ_final[col]) new_name <- paste0(names(ASQ_final[col]), "_dichom") ASQ_final[new_name] <- NA for(i in 1:nrow(ASQ_final)){ ASQ_score <- ASQ_final[[i, c(old_name)]] if(!is.na(ASQ_score)){ if(ASQ_score <= -1){ ASQ_final[[i,c(new_name)]] <- 1 } if(ASQ_score > -1){ ASQ_final[[i,c(new_name)]] <- 0 } } } } ASQ_final$ChildAge_ASQ_months_allchildren_cent <- c(scale(ASQ_final$ChildAge_ASQ_months_allchildren, scale = F)) #select subset of relevant variables ASQ_final <- ASQ_final[,c("participantID", "ChildAge_ASQ_months_allchildren_cent", "Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom", "Child_ASQ_communication_develop_infancy_sum_finalagerange", "Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom", "Child_ASQ_grossmotor_development_infancy_sum_finalagerange", "Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom", "Child_ASQ_finemotor_development_infancy_sum_finalagerange", "Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom", "Child_ASQ_problemsolving_development_infancy_sum_finalagerange", "Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom", "Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange")] ## 7. Merge all data #### #use all the ASQ data ASQ_df1 <- left_join(ASQ_final, q_data_sub_final) ASQ_df2 <- left_join(ASQ_df1, maternal_edu) ASQ_df3 <- left_join(ASQ_df2, postpartum_df_final) ASQ_df_final <- left_join(ASQ_df3, regis_final) ASQ_df_final <- left_join(ASQ_df_final, medication[,c("participantID", "ITUbroadpsychiatricmedication_18_KELA", "antidepressants_18_KELA")], copy=TRUE) ASQ_df_final$ITUbroadpsychiatricmedication_18_KELA <- factor(ASQ_df_final$ITUbroadpsychiatricmedication_18_KELA, levels = c(0,1), labels = c("no", "yes")) ASQ_df_final <- left_join(ASQ_df_final, final_cort) ## 7. Apply Exclusion Criteria #### ASQ_df_final <- subset(ASQ_df_final, !(Maternal_Corticosteroid_Treatment_during_Pregnancy == "yes")) rm(list= ls()[!(ls() %in% c("ASQ_df_final"))]) #Average concentration across pregnancy ASQ_df_final$mean_d_AUC_CAR_only <- rowMeans(ASQ_df_final[,c("d_AUC_CAR_only_I", "d_AUC_CAR_only_II", "d_AUC_CAR_only_III")], na.rm=T) ASQ_df_final$mean_d_AUC_DIUR_only <- rowMeans(ASQ_df_final[,c("cort_d_AUC_noCAR_I", "cort_d_AUC_noCAR_II", "cort_d_AUC_noCAR_III")], na.rm=T) ##### Set up main results table #### ASQ_subscale_results_morning <- setNames(data.frame(matrix(ncol = 19, nrow = 33)), c("Developmental domain", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p")) ASQ_subscale_results_morning[,1] <- c("Gross_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", ".. in Cases", ".. in Controls", "Late Pregnancy", "..in Cases", ".. in Controls", "Pregnancy mean", ".. in Cases", ".. in Controls", "Fine_Motor_Skills", "Early Pregnancy", ".. in boys", ".. in girls", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Communication_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Personal_Social_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Problem_Solving_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean") ##diurnal ASQ_subscale_results_diurnal <- setNames(data.frame(matrix(ncol = 19, nrow = 25)), c("Developmental domain", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p")) ASQ_subscale_results_diurnal[,1] <- c("Gross_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Fine_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Communication_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Personal_Social_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Problem_Solving_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean") ## diurnal Rhythm ASQ_subscale_results_diurnalR <- setNames(data.frame(matrix(ncol = 19, nrow = 15)), c("Developmental domain", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p")) ASQ_subscale_results_diurnalR[,1] <- c("Gross_Motor_Skills", "Morning Decline", "Diurnal Decline", "Fine_Motor_Skills", "Morning Decline", "Diurnal Decline", "Communication_Skills", "Morning Decline", "Diurnal Decline", "Personal_Social_Skills", "Morning Decline", "Diurnal Decline", "Problem_Solving_Skills", "Morning Decline", "Diurnal Decline") ### for neurodevelopmental delay ##### ASQ_subscale_results_morning_ND <- setNames(data.frame(matrix(ncol = 19, nrow = 33)), c("Developmental domain", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p")) ASQ_subscale_results_morning_ND[,1] <- c("Gross_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", ".. in Cases", ".. in Controls", "Late Pregnancy", ".. in Cases", ".. in Controls", "Pregnancy mean", ".. in Cases", ".. in Controls", "Fine_Motor_Skills", "Early Pregnancy", ".. in boys", ".. in girls", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Communication_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Personal_Social_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Problem_Solving_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean") ##diurnal ASQ_subscale_results_diurnal_ND <- setNames(data.frame(matrix(ncol = 19, nrow = 27)), c("Developmental domain", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p")) ASQ_subscale_results_diurnal_ND[,1] <- c("Gross_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Fine_Motor_Skills", "Early Pregnancy", ".. in boys", ".. in girls", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Communication_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Personal_Social_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Problem_Solving_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean") ## diurnal Rhythm ASQ_subscale_results_diurnalR_ND <- setNames(data.frame(matrix(ncol = 19, nrow = 15)), c("Developmental domain", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p")) ASQ_subscale_results_diurnalR_ND[,1] <- c("Gross_Motor_Skills", "Morning Decline", "Diurnal Decline", "Fine_Motor_Skills", "Morning Decline", "Diurnal Decline", "Communication_Skills", "Morning Decline", "Diurnal Decline", "Personal_Social_Skills", "Morning Decline", "Diurnal Decline", "Problem_Solving_Skills", "Morning Decline", "Diurnal Decline") ### Sensitivity Analyses results table #### ASQ_df_final_sub <- subset(ASQ_df_final, !(ITUbroadpsychiatricmedication_18_KELA == "yes")) AppenC_ASQ_subscale_results_morning <- setNames(data.frame(matrix(ncol = 19, nrow = 33)), c("Developmental domain", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p")) AppenC_ASQ_subscale_results_morning[,1] <- c("Gross_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", ".. in Cases", ".. in Controls", "Late Pregnancy", "..in Cases", ".. in Controls", "Pregnancy mean", ".. in Cases", ".. in Controls", "Fine_Motor_Skills", "Early Pregnancy", ".. in boys", ".. in girls", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Communication_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Personal_Social_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Problem_Solving_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean") ##diurnal AppenC_ASQ_subscale_results_diurnal <- setNames(data.frame(matrix(ncol = 19, nrow = 25)), c("Developmental domain", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p")) AppenC_ASQ_subscale_results_diurnal[,1] <- c("Gross_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Fine_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Communication_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Personal_Social_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Problem_Solving_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean") ## diurnal Rhythm AppenC_ASQ_subscale_results_diurnalR <- setNames(data.frame(matrix(ncol = 19, nrow = 15)), c("Developmental domain", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p", "B", "SE", "LL", "UL", "t", "p")) AppenC_ASQ_subscale_results_diurnalR[,1] <- c("Gross_Motor_Skills", "Morning Decline", "Diurnal Decline", "Fine_Motor_Skills", "Morning Decline", "Diurnal Decline", "Communication_Skills", "Morning Decline", "Diurnal Decline", "Personal_Social_Skills", "Morning Decline", "Diurnal Decline", "Problem_Solving_Skills", "Morning Decline", "Diurnal Decline") AppenC_ASQ_subscale_results_morning_ND <- setNames(data.frame(matrix(ncol = 19, nrow = 33)), c("Developmental domain", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p")) AppenC_ASQ_subscale_results_morning_ND[,1] <- c("Gross_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", ".. in Cases", ".. in Controls", "Late Pregnancy", ".. in Cases", ".. in Controls", "Pregnancy mean", ".. in Cases", ".. in Controls", "Fine_Motor_Skills", "Early Pregnancy", ".. in boys", ".. in girls", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Communication_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Personal_Social_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Problem_Solving_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean") ##diurnal AppenC_ASQ_subscale_results_diurnal_ND <- setNames(data.frame(matrix(ncol = 19, nrow = 27)), c("Developmental domain", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p")) AppenC_ASQ_subscale_results_diurnal_ND[,1] <- c("Gross_Motor_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Fine_Motor_Skills", "Early Pregnancy", ".. in boys", ".. in girls", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Communication_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Personal_Social_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean", "Problem_Solving_Skills", "Early Pregnancy", "Mid Pregnancy", "Late Pregnancy", "Pregnancy mean") ## diurnal Rhythm AppenC_ASQ_subscale_results_diurnalR_ND <- setNames(data.frame(matrix(ncol = 19, nrow = 15)), c("Developmental domain", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p", "OR", "SE", "LL", "UL", "t", "p")) AppenC_ASQ_subscale_results_diurnalR_ND[,1] <- c("Gross_Motor_Skills", "Morning Decline", "Diurnal Decline", "Fine_Motor_Skills", "Morning Decline", "Diurnal Decline", "Communication_Skills", "Morning Decline", "Diurnal Decline", "Personal_Social_Skills", "Morning Decline", "Diurnal Decline", "Problem_Solving_Skills", "Morning Decline", "Diurnal Decline") ## Set up functions to report statistics in APA #### ordinal_apa <- function(m,x){ #m=tobit model, x=predictor as displayed in model summary table #document results ctable <- coef(summary(m)) p_val <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ctable <- cbind(ctable, "p value" = p_val) estimate <- format(round(ctable[x,1],2)) SE <- format(round(ctable[x,2],2)) CI <- as.numeric(c(confint(m,x))) LL <- format(round(CI[1],2)) UL <- format(round(CI[2],2)) t <- format(round(ctable[x,3],2)) p <- format(round(ctable[x,4],3)) if(as.numeric(p) < 1 & as.numeric(p) > 0){ p <- snip(as.numeric(p), lead =1) } output <- list(estimate,SE,LL,UL,t,p) names(output) <- c("estimate","SE","LL","UL","t","p") return(output) #output = list object of any parameters that may be interesting to report } logit_apa <- function(m,x){ #m=lobit model, x=predictor as displayed in model summary table #document results estimate <- format(round(exp(coef(summary(m))[x,1]),2)) SE <- format(round(exp(coef(summary(m))[x,2]),2)) CI <- as.numeric(exp(c(confint(m,x)))) LL <- format(round(CI[1],2)) UL <- format(round(CI[2],2)) z <- format(round(coef(summary(m))[x,3],2)) p <- snip(as.numeric(format(round(coef(summary(m))[x,4], 3))), lead = 1) output <- list(estimate,SE,LL,UL,z,p) names(output) <- c("estimate","SE","LL","UL","z","p") return(output) #output = list object of any parameters (of OR) that may be interesting to report } ############################### Ordinal Regression ######################## ########################## Gross Motor Development ########################### ############## Morning Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### ## I #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 # does not converge, hence taking starting values from M1 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, data = ASQ_df_final, na.action = "na.omit", start = c(start_guess), Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ##M3 # does not converge, hence taking starting values from M1 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## by child sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_grossI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## by case VS control M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_grossI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #II #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## by child sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_grossII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## by case VS control M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_grossII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") ## Follow-up #M2 M2_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M2_xCaCo) ctable <- coef(summary(M2_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_grossII.M2 <- ordinal_apa(M2_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #M3 M3_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M3_xCaCo) ctable <- coef(summary(M3_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_grossII.M3 <- ordinal_apa(M3_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") ## for cases M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[4,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[4,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # does not converge, hence taking starting values from M2 start_guess <- c(M2$coefficients, rep(0,2), M2$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[4,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## for controls M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # does not converge, hence taking starting values from M1 start_guess <- c(M2$coefficients, rep(0,2), M2$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #III #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[6,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[6,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[6,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## by child sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_grossIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") ## by case VS control M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_grossIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") ## Follow-up #M2 M2_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M2_xCaCo) ctable <- coef(summary(M2_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_grossIII.M2 <- ordinal_apa(M2_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #M3 M3_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M3_xCaCo) ctable <- coef(summary(M3_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_grossIII.M3 <- ordinal_apa(M3_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") ## for cases M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[7,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[7,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # does not converge, hence taking starting values from M1 start_guess <- c(M2$coefficients, rep(0,2), M2$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[7,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## for controls M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[8,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[8,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # # #M3 # does not converge, hence taking starting values from M1 start_guess <- c(M2$coefficients, rep(0,2), M2$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[8,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[9,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[9,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[9,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MMean_gross <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") # Follow-up M2_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M2_xCaCo) ctable <- coef(summary(M2_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MMean_gross.M2 <- ordinal_apa(M2_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") M3_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M3_xCaCo) ctable <- coef(summary(M3_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MMean_gross.M3 <- ordinal_apa(M3_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") ## for cases M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[10,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[10,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[10,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## for controls M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[11,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[11,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[11,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_gross <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") ASQ_subscale_results_diurnalR[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") ASQ_subscale_results_diurnalR[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") ASQ_subscale_results_diurnalR[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MSlope_gross <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_MSlope_gross <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### ## I #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 # does not converge, hence taking starting values from M1 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, # control=glm.control(maxit=50), data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_grossI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_grossI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") ## II #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_grossII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_grossII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_II") ## III #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[4,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[4,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[4,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_grossIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_grossIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_DMean_gross <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_DMean_gross <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") ASQ_subscale_results_diurnalR[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 #start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) # M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # Diurnal_Slope + # caseVScontrol, #+ # # Maternal_Age_Years_cent + # # Maternal_Education + # # Parity + # # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # # Weight_Gain_cent + # # Maternal_Hypertensive_Disorders_anyVSnone + # # Maternal_Diabetes_Disorders_anyVSnone + # # Maternal_Smoking_During_Pregnancy + # # Gestational_Age_Weeks_cent + # #Child_Birth_Weight_cent, # #control=glm.control(maxit=50), # data = ASQ_df_final, # #start = start_guess, # Hess=TRUE) # summary(M2) # ctable <- coef(summary(M2)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M2,"Diurnal_Slope") # confint(M2)[1] # ASQ_subscale_results_diurnalR[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent + # Diurnal_Slope, # data = ASQ_df_final, # Hess=TRUE) # summary(M3) # ctable <- coef(summary(M3)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M3,"Diurnal_Slope") # ASQ_subscale_results_diurnalR[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### # M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Diurnal_Slope + # Diurnal_Slope:caseVScontrol, # data = ASQ_df_final, # Hess=TRUE) # summary(M1_xCaCo) # ctable <- coef(summary(M1_xCaCo)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # OR_CaCo_DSlope_gross <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Diurnal_Slope") # #Child_Sex #### # M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # Diurnal_Slope + # Diurnal_Slope:Child_Sex, # data = ASQ_df_final, # Hess=TRUE) # summary(M1_xChS) # ctable <- coef(summary(M1_xChS)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # #OR_ChS_DSlope_gross <- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ########################## Fine Motor Development ########################### ############## Morning Cortisol Concentrations and Slope ################# ### Ordinal regression by pregnancy stage #### ## I #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[13,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[13,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[13,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_fineI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_fineI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ### follow-up on child sex #### #M2 M2_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I + Child_Sex:d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M2_xChS) ctable <- coef(summary(M2_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_fineI.M2 <- ordinal_apa(M2_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") #M3 M3_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I + Child_Sex:d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M3_xChS) ctable <- coef(summary(M3_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_fineI.M3 <- ordinal_apa(M3_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## for boys M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[14,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[14,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[14,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## for girls M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[15,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[15,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[15,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## II #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[16,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[16,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[16,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_fineII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_fineII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## III #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[17,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[17,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[17,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_fineIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_fineIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[18,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[18,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[18,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MMean_fine <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_MMean_fine <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") ASQ_subscale_results_diurnalR[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") ASQ_subscale_results_diurnalR[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") ASQ_subscale_results_diurnalR[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MSlope_fine <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_MSlope_fine <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### # I #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[7,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[7,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[7,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_fineI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_fineI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") # II #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[8,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[8,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[8,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_fineII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_fineII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_II") # III #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[9,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[9,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[9,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_fineIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_fineIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[10,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[10,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[10,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_DMean_fine <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_DMean_fine <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") ASQ_subscale_results_diurnalR[6,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, #control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M2,"Diurnal_Slope") # ASQ_subscale_results_diurnalR[6,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #res <- ordinal_apa(M3,"Diurnal_Slope") #ASQ_subscale_results_diurnalR[6,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### # M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Diurnal_Slope + # Diurnal_Slope:caseVScontrol, # data = ASQ_df_final, # Hess=TRUE) # summary(M1_xCaCo) # ctable <- coef(summary(M1_xCaCo)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # OR_CaCo_DSlope_fine <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Diurnal_Slope") # #Child_Sex #### # M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # Diurnal_Slope + # Diurnal_Slope:Child_Sex, # data = ASQ_df_final, # Hess=TRUE) # summary(M1_xChS) # ctable <- coef(summary(M1_xChS)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # OR_ChS_DSlope_fine <- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ########################## Communication skills ################## ############## Morning Cortisol Concentrations and Slope ################# ### Ordinal regression by pregnancy stage #### ## I #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[20,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, data = ASQ_df_final, na.action = "na.omit", start = start_guess, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[20,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[20,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_comI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_comI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## II #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[21,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[21,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[21,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_comII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_comII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## III #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[22,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[22,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[22,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_comIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_comIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[23,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[23,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[23,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MMean_com <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_MMean_com <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") ASQ_subscale_results_diurnalR[8,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") ASQ_subscale_results_diurnalR[8,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") ASQ_subscale_results_diurnalR[8,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MSlope_com <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_MSlope_com <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### # I #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[12,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, # control=glm.control(maxit=50), data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[12,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[12,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_comI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_comI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") # II #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[13,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") #ASQ_subscale_results_diurnal[13,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[13,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_comII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_comII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_II") # III #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[14,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[14,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") res <- c(ctable["cort_d_AUC_noCAR_III",]) ASQ_subscale_results_diurnal[14,c(14:15, 18:19)] <- res # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_comIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_comIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[15,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[15,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[15,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_DMean_com <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_DMean_com <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") ASQ_subscale_results_diurnalR[9,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, #control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Diurnal_Slope") ASQ_subscale_results_diurnalR[9,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Diurnal_Slope") ASQ_subscale_results_diurnalR[9,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_DSlope_com <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Diurnal_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #OR_ChS_DSlope_com <- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ########################## Personal Social Skills ################# ############## Morning Cortisol Concentrations and Slope ################# ### Ordinal regression by pregnancy stage #### ## I #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[25,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #class(factor(ASQ_df_final$Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange, ordered=T)) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.omit", Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[25,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[25,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_perI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_perI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## II #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[26,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[26,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[26,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_perII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_perII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## III #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[27,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[27,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[27,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_perIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_perIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[28,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[28,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[28,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MMean_per <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_MMean_per <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") ASQ_subscale_results_diurnalR[11,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") ASQ_subscale_results_diurnalR[11,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") ASQ_subscale_results_diurnalR[11,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MSlope_per <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_MSlope_per <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### # I #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[17,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[17,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[17,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # # Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_perI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_perI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") # II #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[18,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[18,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[18,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_perII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_perII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_II") # III #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[19,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[19,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[19,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_perIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_perIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[20,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[20,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[20,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_DMean_per <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_DMean_per <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") ASQ_subscale_results_diurnalR[12,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) # M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # Diurnal_Slope + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent, # #control=glm.control(maxit=50), # data = ASQ_df_final, # start = start_guess, # na.action = "na.omit", # Hess=TRUE) # summary(M2) # ctable <- coef(summary(M2)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M2,"Diurnal_Slope") # ASQ_subscale_results_diurnalR[12,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent + # Diurnal_Slope, # data = ASQ_df_final, # Hess=TRUE) # summary(M3) # ctable <- coef(summary(M3)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M3,"Diurnal_Slope") # ASQ_subscale_results_diurnalR[12,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### # #caseVScontrol #### start_guess <- c(M1$coefficients, rep(0,2), M1$zeta) M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + caseVScontrol + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_DSlope_per <- ordinal_apa(M1_xCaCo,"Diurnal_Slope:caseVScontrolcontrol") #Child_Sex #### start_guess <- c(M1$coefficients, 0, M1$zeta) M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_DSlope_per<- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ########################## Problem Solving #################### ############## Morning Cortisol Concentrations and Slope ################# ### Ordinal regression by pregnancy stage #### ## I #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[30,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #class(factor(ASQ_df_final$Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T)) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.omit", Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") #confint(M2) ASQ_subscale_results_morning[30,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_I") ASQ_subscale_results_morning[30,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_probI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_probI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## II #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[31,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[31,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") ASQ_subscale_results_morning[31,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_probII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_probII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## III #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[32,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[32,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") ASQ_subscale_results_morning[32,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_morning_probIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_morning_probIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[33,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[33,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning[33,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MMean_prob <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_MMean_prob <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") ASQ_subscale_results_diurnalR[14,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") ASQ_subscale_results_diurnalR[14,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") ASQ_subscale_results_diurnalR[14,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_MSlope_prob <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_MSlope_prob <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### # I #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[22,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[22,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal[22,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_probI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_probI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") # II #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[23,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[23,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal[23,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_probII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_probII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_II") # III #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[24,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[24,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal[24,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_diurnal_probIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex start_guess <- c(M1$coefficients, rep(0,1), M1$zeta) M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_diurnal_probIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[25,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[25,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal[25,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_DMean_prob <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_DMean_prob <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") ASQ_subscale_results_diurnalR[15,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, #control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Diurnal_Slope") ASQ_subscale_results_diurnalR[15,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Diurnal_Slope") ASQ_subscale_results_diurnalR[15,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### start_guess <- c(M1$coefficients, rep(0,2), M1$zeta) M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + caseVScontrol + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_CaCo_DSlope_prob <- ordinal_apa(M1_xCaCo,"Diurnal_Slope:caseVScontrolcontrol") #Child_Sex #### start_guess <- c(M1$coefficients, rep(0,1), M1$zeta) M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) OR_ChS_DSlope_prob <- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ############################### Logistic Regression ######################## ########################## Gross Motor Development ########################### ######### Morning Cortisol Concentrations and Slope #### #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[2,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_I,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # ## end #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSI_gross <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoI_gross <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[2,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[2,14:19] <- res # II #### #M1 M1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[3,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_I,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[3,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[3,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSII_gross <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoII_gross <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") ### follow-up on cases vs controls #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") logit.res__CARconxCaCoII_gross.M2 <- logit_apa(M2_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results logit.res__CARconxCaCoII_gross.M3 <- logit_apa(M3_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") ## for cases #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M1_INT2_CARcon) res <- logit_apa(M1_INT2_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[4,2:7] <- res ### follow-up on cases vs controls #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[4,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[4,14:19] <- res ## for controls #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M1_INT2_CARcon) res <- logit_apa(M1_INT2_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[5,2:7] <- res ### follow-up on cases vs controls #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[5,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[5,14:19] <- res #III #### #M1 M1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[6,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_I,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[6,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[6,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSIII_gross <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoIII_gross <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") ### follow-up on cases vs controls #### #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") logit.res__CARconxCaCoIII_gross.M2 <- logit_apa(M2_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results logit.res__CARconxCaCoIII_gross.M3 <- logit_apa(M3_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") ## for cases #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M1_INT2_CARcon) res <- logit_apa(M1_INT2_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[7,2:7] <- res ### follow-up on cases vs controls #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[7,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[7,14:19] <- res ## for controls #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M1_INT2_CARcon) res <- logit_apa(M1_INT2_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[8,2:7] <- res #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[8,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[8,14:19] <- res #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[9,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) logit.res_MeanconxChS_gross <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) logit.res_MeanconxCaCo_gross <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") ## M2 Interaction when controlling for other covariates M2_cov_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) logit.res_MeanconxCaCo_gross_M2 <- logit_apa(M2_cov_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M3 M3_cov_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) logit.res_MeanconxCaCo_gross_M3 <- logit_apa(M3_cov_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") ##without interaction #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[9,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[9,14:19] <- res ## Follow-up on caseVScontrol interaction #### ##for cases M1_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M1_INT2_Meancon) res <- logit_apa(M1_INT2_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[10,2:7] <- res #M2 M2_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M2_INT2_Meancon) res <- logit_apa(M2_INT2_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[10,8:13] <- res #M3 M3_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M3_INT2_Meancon) res <- logit_apa(M3_INT2_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[10,14:19] <- res ##for controls M1_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M1_INT2_Meancon) res <- logit_apa(M1_INT2_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[11,2:7] <- res #M2 M2_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M2_INT2_Meancon) res <- logit_apa(M2_INT2_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[11,8:13] <- res #M3 M3_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M3_INT2_Meancon) res <- logit_apa(M3_INT2_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[11,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[2,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) logit.res_mornSlopexChS_gross <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) logit.res_mornSlopexCaCo_gross <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[2,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[2,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[2,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[2,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[2,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSI_gross <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoI_gross <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # II #### #M1 M1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[3,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[3,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[3,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSII_gross <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoII_gross <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") # III #### #M1 M1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[4,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[4,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[4,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSIII_gross <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoIII_gross <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[5,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) logit.res_DMeanconxChS_gross <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) logit.res_DMeanconxCaCo_gross <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[5,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[5,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[3,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) logit.res_DIURSlopexChS_gross <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) logit.res_DIURSlopexCaCo_gross <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[3,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[3,14:19] <- res ########################## Fine Motor Development ########################### ######### Morning Cortisol Concentrations and Slope #### # assumptions ### # new_ASQ.total <- na.omit(ASQ_df_final[,-c(4, 7:18,22)]) #first exclude the pregnancy-stage specific predictors # # # Fit the logistic regression model # model <- glm(Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent, # data = new_ASQ.total, # family = binomial, # na.action = "na.exclude") # # # Predict the probability (p) of diabete positivity # probabilities <- predict(model, type = "response") # # predicted.classes <- ifelse(probabilities > 0.3, "pos", "neg") # # head(predicted.classes) # # Select only numeric predictors # mydata <- new_ASQ.total %>% # dplyr::select_if(is.numeric) # #mydata <- mydata[,-c(5:10)] # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # # ## Cook's distance # plot(model, which = 4, id.n = 3) # # #influential cases # # Extract model results # #new_ASQ <- ASQ_df_final[,-c(10:14)] #first exclude the other outcome variables # model.data <- augment(model, data = new_ASQ.total) %>% mutate(index = 1:n()) # outliers <- model.data %>% top_n(3, .cooksd) #with with neurodevelopmental delay, high depression and anxiety (two controls, one case) # # #plot standardize residuals # ggplot(model.data, aes(index, .std.resid)) + # geom_point(aes(color = Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom), alpha = .5) + # theme_bw() # model.data %>% # filter(abs(.std.resid) > 3) #no influential cases # # #multicollinearity # car::vif(model) #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[13,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[13,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[13,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSI_fine <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") ## Follow-up on sig. interaction #### #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results logit.res_CARconxChSI_fine.M2 <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) logit.res_CARconxChSI_fine.M3 <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") ## For boys #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M1_CARcon) res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[14,2:7] <- res #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + #Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I + d_AUC_CAR_only_II + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[14,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], control=glm.control(maxit=50), family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[14,14:19] <- res #predictor of interest itself leads to fitted probabilities of 0 or 1 ## For girls #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M1_CARcon) res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[15,2:7] <- res #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[15,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], control=glm.control(maxit=50), family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[15,14:19] <- res #fitted probabilities would lead to 0 or 1 # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoI_fine <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") # II #### #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[16,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[16,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[16,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSII_fine <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoII_fine <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") # III #### #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[17,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[17,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[17,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSIII_fine <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoIII_fine <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[18,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) logit.res_MeanconxChS_fine <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) logit.res_MeanconxCaCo_fine <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[18,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[18,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[5,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) logit.res_mornSlopexChS_fine <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) logit.res_mornSlopexCaCo__fine <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[5,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[5,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[7,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[7,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[7,14:19] <- res ### interactions ##### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSI_fine <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") ### Follow-up on child sex interaction M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) logit.res_DIURconxChSI_fine.M2 <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) logit.res_DIURconxChSI_fine.M3 <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") ### for boys #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M1_INT1_DIURcon) res <- logit_apa(M1_INT1_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[8,2:7] <- res #M2 M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, control=glm.control(maxit=50), data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M2_cov_DIURcon) res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[8,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M3_cov_DIURcon) res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[8,14:19] <- res ### for girls #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M1_INT1_DIURcon) res <- logit_apa(M1_INT1_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[9,2:7] <- res M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M2_cov_DIURcon) res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[9,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M3_cov_DIURcon) res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[9,14:19] <- res #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoI_fine <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # II #### #M1 M1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[10,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[10,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[10,14:19] <- res ### interactions ##### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSII_fine <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoII_fine <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") # III #### #M1 M1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[11,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[11,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[11,14:19] <- res ### interactions ##### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSIII_fine <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoIII_fine <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[12,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) logit.res_DMeanconxChS_fine <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) logit.res_DMeanconxCaCo_fine <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[12,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[12,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[6,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) logit.res_DIURSlopexChS_fine <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) logit.res_DIURSlopexCaCo_fine <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[6,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[6,14:19] <- res ########################## Communication skills ################## ######### Morning Cortisol Concentrations and Slope #### # assumptions ### # new_ASQ.total <- na.omit(ASQ_df_final[,-c(4, 7:18,22)]) #first exclude the pregnancy-stage specific predictors # # # Fit the logistic regression model # model <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent, # data = new_ASQ.total, # family = binomial, # na.action = "na.exclude") # # # Predict the probability (p) of diabete positivity # probabilities <- predict(model, type = "response") # # predicted.classes <- ifelse(probabilities > 0.3, "pos", "neg") # # head(predicted.classes) # # Select only numeric predictors # mydata <- new_ASQ.total %>% # dplyr::select_if(is.numeric) # #mydata <- mydata[,-c(5:10)] # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # # ## Cook's distance # plot(model, which = 4, id.n = 3) # # #influential cases # # Extract model results # #new_ASQ <- ASQ_df_final[,-c(10:14)] #first exclude the other outcome variables # model.data <- augment(model, data = new_ASQ.total) %>% mutate(index = 1:n()) # outliers <- model.data %>% top_n(3, .cooksd) #with with neurodevelopmental delay, high depression and anxiety (two controls, one case) # # #plot standardize residuals # ggplot(model.data, aes(index, .std.resid)) + # geom_point(aes(color = Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom), alpha = .5) + # theme_bw() # model.data %>% # filter(abs(.std.resid) > 3) #no influential cases # # #multicollinearity # car::vif(model) #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[20,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[20,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[20,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSI_com <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res_CARconxCaCoI_com <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") # II #### #M1 M1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[21,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[21,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[21,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSII_com <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res_CARconxCaCoII_com <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") # III #### #M1 M1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[22,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[22,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[22,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSIII_com <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res_CARconxCaCoIII_com <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[23,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) logit.res_MeanconxChS_com <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) logit.res_MeanconxCaCo_com <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[23,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[23,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[8,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) logit.res_mornSlopexChS_com <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) logit.res_mornSlopexCaCo_com <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[8,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[8,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[14,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[14,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[14,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSI_com <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoI_com <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # II #### #M1 M1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[15,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[15,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[15,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSII_com <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoII_com <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") # III #### #M1 M1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[16,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[16,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[16,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSIII_com <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoIII_com <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[17,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) logit.res_DMeanconxChS_com <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) logit.res_DMeanconxCaCo_com <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[17,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[17,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[9,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) logit.res_DIURSlopexChS_com <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) logit.res_DIURSlopexCaCo_com <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[9,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[9,14:19] <- res ########################## Personal Social Skills ################# ######### Morning Cortisol Concentrations and Slope #### # assumptions ### # new_ASQ.total <- na.omit(ASQ_df_final[,-c(4, 7:18,22)]) #first exclude the pregnancy-stage specific predictors # # # Fit the logistic regression model # model <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent, # data = new_ASQ.total, # family = binomial, # na.action = "na.exclude") # # # Predict the probability (p) of diabete positivity # probabilities <- predict(model, type = "response") # # predicted.classes <- ifelse(probabilities > 0.3, "pos", "neg") # # head(predicted.classes) # # Select only numeric predictors # mydata <- new_ASQ.total %>% # dplyr::select_if(is.numeric) # #mydata <- mydata[,-c(5:10)] # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # # ## Cook's distance # plot(model, which = 4, id.n = 3) # # #influential cases # # Extract model results # #new_ASQ <- ASQ_df_final[,-c(10:14)] #first exclude the other outcome variables # model.data <- augment(model, data = new_ASQ.total) %>% mutate(index = 1:n()) # outliers <- model.data %>% top_n(3, .cooksd) #with with neurodevelopmental delay, high depression and anxiety (two controls, one case) # # #plot standardize residuals # ggplot(model.data, aes(index, .std.resid)) + # geom_point(aes(color = Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom), alpha = .5) + # theme_bw() # model.data %>% # filter(abs(.std.resid) > 3) #no influential cases # # #multicollinearity # car::vif(model) #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[25,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[25,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[25,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSI_per <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoI_per <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") # II #### #M1 M1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[26,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[26,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[26,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSII_per <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoII_per <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") # III #### #M1 M1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[27,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[27,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[27,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSIII_per <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoIII_per <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[28,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) logit.res_MeanconxChS_per <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) logit.res_MeanconxCaCo_per <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[28,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[28,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[11,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) logit.res_mornSlopexChS_per <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) logit.res_mornSlopexCaCo_per <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[11,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[11,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[19,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[19,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[19,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSI_per <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoI_per <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") ## II #### #M1 M1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[20,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[20,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[20,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSII_per <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoII_per <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") ## III #### #M1 M1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[21,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[21,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[21,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSIII_per <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoIII_per <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[22,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) logit.res_DMeanconxChS_per <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) logit.res_DMeanconxCaCo_per <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[22,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[22,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[12,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) logit.res_DIURSlopexChS_per <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) logit.res_DIURSlopexCaCo_per <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[12,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[12,14:19] <- res ########################## Problem Solving #################### ######### Morning Cortisol Concentrations and Slope #### # assumptions ### # new_ASQ.total <- na.omit(ASQ_df_final[,-c(4, 7:18,22)]) #first exclude the pregnancy-stage specific predictors # # # Fit the logistic regression model # model <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent, # data = new_ASQ.total, # family = binomial, # na.action = "na.exclude") # # # Predict the probability (p) of diabete positivity # probabilities <- predict(model, type = "response") # # predicted.classes <- ifelse(probabilities > 0.3, "pos", "neg") # # head(predicted.classes) # # Select only numeric predictors # mydata <- new_ASQ.total %>% # dplyr::select_if(is.numeric) # #mydata <- mydata[,-c(5:10)] # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # # ## Cook's distance # plot(model, which = 4, id.n = 3) # # #influential cases # # Extract model results # #new_ASQ <- ASQ_df_final[,-c(10:14)] #first exclude the other outcome variables # model.data <- augment(model, data = new_ASQ.total) %>% mutate(index = 1:n()) # outliers <- model.data %>% top_n(3, .cooksd) #with with neurodevelopmental delay, high depression and anxiety (two controls, one case) # # #plot standardize residuals # ggplot(model.data, aes(index, .std.resid)) + # geom_point(aes(color = Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom), alpha = .5) + # theme_bw() # model.data %>% # filter(abs(.std.resid) > 3) #no influential cases # # #multicollinearity # car::vif(model) #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[30,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[30,8:13] <- res #M3 ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") ASQ_subscale_results_morning_ND[30,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSI_prob <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res__CARconxCaCoI_prob <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") # II #### #M1 M1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[31,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[31,8:13] <- res #M3 ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") ASQ_subscale_results_morning_ND[31,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSII_prob <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res_CARconxCaCoII_prob <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") # III #### #M1 M1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[32,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[32,8:13] <- res #M3 ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") ASQ_subscale_results_morning_ND[32,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) logit.res_CARconxChSIII_prob <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) logit.res_CARconxCaCoIII_prob <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[33,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) logit.res_MeanconxChS_prob <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) logit.res_MeanconxCaCo_prob <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[33,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") ASQ_subscale_results_morning_ND[33,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[14,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) logit.res_mornSlopexChS_prob <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) logit.res_mornSlopexCaCo_prob <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[14,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") ASQ_subscale_results_diurnalR_ND[14,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[24,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[24,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") ASQ_subscale_results_diurnal_ND[24,14:19] <- res ### interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSI_prob <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoI_prob <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # II #### #M1 M1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[25,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[25,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") ASQ_subscale_results_diurnal_ND[25,14:19] <- res ### interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSII_prob <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoII_prob <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") # III #### #M1 M1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[26,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[26,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") ASQ_subscale_results_diurnal_ND[26,14:19] <- res ### interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) logit.res_DIURconxChSIII_prob <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) logit.res_DIURconxCaCoIII_prob <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[27,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) logit.res_DMeanconxChS_prob <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) logit.res_DMeanconxCaCo_prob <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[27,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") ASQ_subscale_results_diurnal_ND[27,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[15,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) logit.res_DIURSlopexChS_prob <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) logit.res_DIURSlopexCaCo_prob <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[15,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") ASQ_subscale_results_diurnalR_ND[15,14:19] <- res ##### interaction tables ##### ## gross motor development ##### ASQ_gross_results_INT <- setNames(data.frame(matrix(ncol = 9, nrow = 22)), c("Cortisol Index", "B", "SE", #"LL", #"UL", "z", "p", "OR", "SE", #"LL", #"UL", "z", "p")) ASQ_gross_results_INT[,1] <- c("By Child Sex", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope", "By Case VS Control", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope") #score #ChS ASQ_gross_results_INT[2,2:5] <- OR_ChS_morning_grossI[c("estimate","SE","t", "p")] ASQ_gross_results_INT[3,2:5] <- OR_ChS_morning_grossII[c("estimate","SE","t", "p")] ASQ_gross_results_INT[4,2:5] <- OR_ChS_morning_grossIII[c("estimate","SE","t", "p")] #ASQ_gross_results_INT[5,2:5] <- OR_ChS_MMean_gross[c("estimate","SE","t", "p")] ASQ_gross_results_INT[6,2:5] <- OR_ChS_MSlope_gross[c("estimate","SE","t", "p")] ASQ_gross_results_INT[7,2:5] <- OR_ChS_diurnal_grossI[c("estimate","SE","t", "p")] ASQ_gross_results_INT[8,2:5] <- OR_ChS_diurnal_grossII[c("estimate","SE","t", "p")] ASQ_gross_results_INT[9,2:5] <- OR_ChS_diurnal_grossIII[c("estimate","SE","t", "p")] ASQ_gross_results_INT[10,2:5] <- OR_ChS_DMean_gross[c("estimate","SE","t", "p")] #ASQ_gross_results_INT[11,2:5] <- OR_ChS_DSlope_gross[c("estimate","SE","t", "p")] #CaCo ASQ_gross_results_INT[13,2:5] <- OR_CaCo_morning_grossI[c("estimate","SE","t", "p")] ASQ_gross_results_INT[14,2:5] <- OR_CaCo_morning_grossII[c("estimate","SE","t", "p")] ASQ_gross_results_INT[15,2:5] <- OR_CaCo_morning_grossIII[c("estimate","SE","t", "p")] ASQ_gross_results_INT[16,2:5] <- OR_CaCo_MMean_gross[c("estimate","SE","t", "p")] ASQ_gross_results_INT[17,2:5] <- OR_CaCo_MSlope_gross[c("estimate","SE","t", "p")] ASQ_gross_results_INT[18,2:5] <- OR_CaCo_diurnal_grossI[c("estimate","SE","t", "p")] ASQ_gross_results_INT[19,2:5] <- OR_CaCo_diurnal_grossII[c("estimate","SE","t", "p")] ASQ_gross_results_INT[20,2:5] <- OR_CaCo_diurnal_grossIII[c("estimate","SE","t", "p")] ASQ_gross_results_INT[21,2:5] <- OR_CaCo_DMean_gross[c("estimate","SE","t", "p")] #ASQ_gross_results_INT[22,2:5] <- OR_CaCo_DSlope_gross[c("estimate","SE","t", "p")] ##delay #ChS ASQ_gross_results_INT[2,6:9] <-logit.res_CARconxChSI_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[3,6:9] <-logit.res_CARconxChSII_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[4,6:9] <-logit.res_CARconxChSIII_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[5,6:9] <-logit.res_MeanconxChS_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[6,6:9] <-logit.res_mornSlopexChS_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[7,6:9] <-logit.res_DIURconxChSI_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[8,6:9] <-logit.res_DIURconxChSII_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[9,6:9] <-logit.res_DIURconxChSIII_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[10,6:9] <- logit.res_DMeanconxChS_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[11,6:9] <-logit.res_DIURSlopexChS_gross[c("estimate","SE","z", "p")] #CaCo ASQ_gross_results_INT[13,6:9] <- logit.res__CARconxCaCoI_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[14,6:9] <- logit.res__CARconxCaCoII_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[15,6:9] <- logit.res__CARconxCaCoIII_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[16,6:9] <- logit.res_MeanconxCaCo_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[17,6:9] <- logit.res_mornSlopexCaCo_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[18,6:9] <- logit.res_DIURconxCaCoI_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[19,6:9] <- logit.res_DIURconxCaCoII_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[20,6:9] <- logit.res_DIURconxCaCoIII_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[21,6:9] <- logit.res_DMeanconxCaCo_gross[c("estimate","SE","z", "p")] ASQ_gross_results_INT[22,6:9] <-logit.res_DIURSlopexCaCo_gross[c("estimate","SE","z", "p")] ## fine motor development ##### ASQ_fine_results_INT <- setNames(data.frame(matrix(ncol = 9, nrow = 22)), c("Cortisol Index", "B", "SE", #"LL", #"UL", "z", "p", "OR", "SE", #"LL", #"UL", "z", "p")) ASQ_fine_results_INT[,1] <- c("By Child Sex", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope", "By Case VS Control", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope") #score #ChS ASQ_fine_results_INT[2,2:5] <- OR_ChS_morning_fineI[c("estimate","SE","t", "p")] ASQ_fine_results_INT[3,2:5] <- OR_ChS_morning_fineII[c("estimate","SE","t", "p")] ASQ_fine_results_INT[4,2:5] <- OR_ChS_morning_fineIII[c("estimate","SE","t", "p")] ASQ_fine_results_INT[5,2:5] <- OR_ChS_MMean_fine[c("estimate","SE","t", "p")] ASQ_fine_results_INT[6,2:5] <- OR_ChS_MSlope_fine[c("estimate","SE","t", "p")] ASQ_fine_results_INT[7,2:5] <- OR_ChS_diurnal_fineI[c("estimate","SE","t", "p")] ASQ_fine_results_INT[8,2:5] <- OR_ChS_diurnal_fineII[c("estimate","SE","t", "p")] ASQ_fine_results_INT[9,2:5] <- OR_ChS_diurnal_fineIII[c("estimate","SE","t", "p")] ASQ_fine_results_INT[10,2:5] <- OR_ChS_DMean_fine[c("estimate","SE","t", "p")] #ASQ_fine_results_INT[11,2:5] <- OR_ChS_DSlope_fine[c("estimate","SE","t", "p")] #CaCo ASQ_fine_results_INT[13,2:5] <- OR_CaCo_morning_fineI[c("estimate","SE","t", "p")] ASQ_fine_results_INT[14,2:5] <- OR_CaCo_morning_fineII[c("estimate","SE","t", "p")] ASQ_fine_results_INT[15,2:5] <- OR_CaCo_morning_fineIII[c("estimate","SE","t", "p")] ASQ_fine_results_INT[16,2:5] <- OR_CaCo_MMean_fine[c("estimate","SE","t", "p")] ASQ_fine_results_INT[17,2:5] <- OR_CaCo_MSlope_fine[c("estimate","SE","t", "p")] ASQ_fine_results_INT[18,2:5] <- OR_CaCo_diurnal_fineI[c("estimate","SE","t", "p")] ASQ_fine_results_INT[19,2:5] <- OR_CaCo_diurnal_fineII[c("estimate","SE","t", "p")] ASQ_fine_results_INT[20,2:5] <- OR_CaCo_diurnal_fineIII[c("estimate","SE","t", "p")] ASQ_fine_results_INT[21,2:5] <- OR_CaCo_DMean_fine[c("estimate","SE","t", "p")] #ASQ_fine_results_INT[22,2:5] <- OR_CaCo_DSlope_fine[c("estimate","SE","t", "p")] ##delay #ChS ASQ_fine_results_INT[2,6:9] <-logit.res_CARconxChSI_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[3,6:9] <-logit.res_CARconxChSII_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[4,6:9] <-logit.res_CARconxChSIII_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[5,6:9] <-logit.res_MeanconxChS_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[6,6:9] <-logit.res_mornSlopexChS_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[7,6:9] <-logit.res_DIURconxChSI_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[8,6:9] <-logit.res_DIURconxChSII_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[9,6:9] <-logit.res_DIURconxChSIII_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[10,6:9] <- logit.res_DMeanconxChS_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[11,6:9] <-logit.res_DIURSlopexChS_fine[c("estimate","SE","z", "p")] #CaCo ASQ_fine_results_INT[13,6:9] <- logit.res__CARconxCaCoI_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[14,6:9] <- logit.res__CARconxCaCoII_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[15,6:9] <- logit.res__CARconxCaCoIII_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[16,6:9] <- logit.res_MeanconxCaCo_fine[c("estimate","SE","z", "p")] #ASQ_fine_results_INT[17,6:9] <- logit.res_mornSlopexCaCo_fine[c("estimate","SE","z", "p")] #ASQ_fine_results_INT[18,6:9] <- logit.res_DIURconxCaCoI_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[19,6:9] <- logit.res_DIURconxCaCoII_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[20,6:9] <- logit.res_DIURconxCaCoIII_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[21,6:9] <- logit.res_DMeanconxCaCo_fine[c("estimate","SE","z", "p")] ASQ_fine_results_INT[22,6:9] <-logit.res_DIURSlopexCaCo_fine[c("estimate","SE","z", "p")] ## communication development ##### ASQ_com_results_INT <- setNames(data.frame(matrix(ncol = 9, nrow = 22)), c("Cortisol Index", "B", "SE", #"LL", #"UL", "z", "p", "OR", "SE", #"LL", #"UL", "z", "p")) ASQ_com_results_INT[,1] <- c("By Child Sex", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope", "By Case VS Control", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope") #score #ChS ASQ_com_results_INT[2,2:5] <- OR_ChS_morning_comI[c("estimate","SE","t", "p")] ASQ_com_results_INT[3,2:5] <- OR_ChS_morning_comII[c("estimate","SE","t", "p")] ASQ_com_results_INT[4,2:5] <- OR_ChS_morning_comIII[c("estimate","SE","t", "p")] ASQ_com_results_INT[5,2:5] <- OR_ChS_MMean_com[c("estimate","SE","t", "p")] ASQ_com_results_INT[6,2:5] <- OR_ChS_MSlope_com[c("estimate","SE","t", "p")] ASQ_com_results_INT[7,2:5] <- OR_ChS_diurnal_comI[c("estimate","SE","t", "p")] ASQ_com_results_INT[8,2:5] <- OR_ChS_diurnal_comII[c("estimate","SE","t", "p")] ASQ_com_results_INT[9,2:5] <- OR_ChS_diurnal_comIII[c("estimate","SE","t", "p")] ASQ_com_results_INT[10,2:5] <- OR_ChS_DMean_com[c("estimate","SE","t", "p")] #ASQ_com_results_INT[11,2:5] <- OR_ChS_DSlope_com[c("estimate","SE","t", "p")] #CaCo ASQ_com_results_INT[13,2:5] <- OR_CaCo_morning_comI[c("estimate","SE","t", "p")] ASQ_com_results_INT[14,2:5] <- OR_CaCo_morning_comII[c("estimate","SE","t", "p")] ASQ_com_results_INT[15,2:5] <- OR_CaCo_morning_comIII[c("estimate","SE","t", "p")] ASQ_com_results_INT[16,2:5] <- OR_CaCo_MMean_com[c("estimate","SE","t", "p")] ASQ_com_results_INT[17,2:5] <- OR_CaCo_MSlope_com[c("estimate","SE","t", "p")] ASQ_com_results_INT[18,2:5] <- OR_CaCo_diurnal_comI[c("estimate","SE","t", "p")] ASQ_com_results_INT[19,2:5] <- OR_CaCo_diurnal_comII[c("estimate","SE","t", "p")] ASQ_com_results_INT[20,2:5] <- OR_CaCo_diurnal_comIII[c("estimate","SE","t", "p")] ASQ_com_results_INT[21,2:5] <- OR_CaCo_DMean_com[c("estimate","SE","t", "p")] ASQ_com_results_INT[22,2:5] <- OR_CaCo_DSlope_com[c("estimate","SE","t", "p")] ##delay #ChS ASQ_com_results_INT[2,6:9] <-logit.res_CARconxChSI_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[3,6:9] <-logit.res_CARconxChSII_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[4,6:9] <-logit.res_CARconxChSIII_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[5,6:9] <-logit.res_MeanconxChS_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[6,6:9] <-logit.res_mornSlopexChS_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[7,6:9] <-logit.res_DIURconxChSI_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[8,6:9] <-logit.res_DIURconxChSII_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[9,6:9] <-logit.res_DIURconxChSIII_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[10,6:9] <- logit.res_DMeanconxChS_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[11,6:9] <-logit.res_DIURSlopexChS_com[c("estimate","SE","z", "p")] #CaCo ASQ_com_results_INT[13,6:9] <- logit.res_CARconxCaCoI_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[14,6:9] <- logit.res_CARconxCaCoII_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[15,6:9] <- logit.res_CARconxCaCoIII_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[16,6:9] <- logit.res_MeanconxCaCo_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[17,6:9] <- logit.res_mornSlopexCaCo_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[18,6:9] <- logit.res_DIURconxCaCoI_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[19,6:9] <- logit.res_DIURconxCaCoII_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[20,6:9] <- logit.res_DIURconxCaCoIII_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[21,6:9] <- logit.res_DMeanconxCaCo_com[c("estimate","SE","z", "p")] ASQ_com_results_INT[22,6:9] <-logit.res_DIURSlopexCaCo_com[c("estimate","SE","z", "p")] ## personal/social development ##### ASQ_per_results_INT <- setNames(data.frame(matrix(ncol = 9, nrow = 22)), c("Cortisol Index", "B", "SE", #"LL", #"UL", "z", "p", "OR", "SE", #"LL", #"UL", "z", "p")) ASQ_per_results_INT[,1] <- c("By Child Sex", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope", "By Case VS Control", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope") #score #ChS ASQ_per_results_INT[2,2:5] <- OR_ChS_morning_perI[c("estimate","SE","t", "p")] ASQ_per_results_INT[3,2:5] <- OR_ChS_morning_perII[c("estimate","SE","t", "p")] ASQ_per_results_INT[4,2:5] <- OR_ChS_morning_perIII[c("estimate","SE","t", "p")] ASQ_per_results_INT[5,2:5] <- OR_ChS_MMean_per[c("estimate","SE","t", "p")] ASQ_per_results_INT[6,2:5] <- OR_ChS_MSlope_per[c("estimate","SE","t", "p")] ASQ_per_results_INT[7,2:5] <- OR_ChS_diurnal_perI[c("estimate","SE","t", "p")] ASQ_per_results_INT[8,2:5] <- OR_ChS_diurnal_perII[c("estimate","SE","t", "p")] ASQ_per_results_INT[9,2:5] <- OR_ChS_diurnal_perIII[c("estimate","SE","t", "p")] ASQ_per_results_INT[10,2:5] <- OR_ChS_DMean_per[c("estimate","SE","t", "p")] ASQ_per_results_INT[11,2:5] <- OR_ChS_DSlope_per[c("estimate","SE","t", "p")] #CaCo ASQ_per_results_INT[13,2:5] <- OR_CaCo_morning_perI[c("estimate","SE","t", "p")] ASQ_per_results_INT[14,2:5] <- OR_CaCo_morning_perII[c("estimate","SE","t", "p")] ASQ_per_results_INT[15,2:5] <- OR_CaCo_morning_perIII[c("estimate","SE","t", "p")] ASQ_per_results_INT[16,2:5] <- OR_CaCo_MMean_per[c("estimate","SE","t", "p")] ASQ_per_results_INT[17,2:5] <- OR_CaCo_MSlope_per[c("estimate","SE","t", "p")] ASQ_per_results_INT[18,2:5] <- OR_CaCo_diurnal_perI[c("estimate","SE","t", "p")] ASQ_per_results_INT[19,2:5] <- OR_CaCo_diurnal_perII[c("estimate","SE","t", "p")] ASQ_per_results_INT[20,2:5] <- OR_CaCo_diurnal_perIII[c("estimate","SE","t", "p")] ASQ_per_results_INT[21,2:5] <- OR_CaCo_DMean_per[c("estimate","SE","t", "p")] ASQ_per_results_INT[22,2:5] <- OR_CaCo_DSlope_per[c("estimate","SE","t", "p")] ##delay #ChS ASQ_per_results_INT[2,6:9] <-logit.res_CARconxChSI_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[3,6:9] <-logit.res_CARconxChSII_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[4,6:9] <-logit.res_CARconxChSIII_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[5,6:9] <-logit.res_MeanconxChS_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[6,6:9] <-logit.res_mornSlopexChS_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[7,6:9] <-logit.res_DIURconxChSI_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[8,6:9] <-logit.res_DIURconxChSII_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[9,6:9] <-logit.res_DIURconxChSIII_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[10,6:9] <- logit.res_DMeanconxChS_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[11,6:9] <-logit.res_DIURSlopexChS_per[c("estimate","SE","z", "p")] #CaCo ASQ_per_results_INT[13,6:9] <- logit.res__CARconxCaCoI_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[14,6:9] <- logit.res__CARconxCaCoII_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[15,6:9] <- logit.res__CARconxCaCoIII_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[16,6:9] <- logit.res_MeanconxCaCo_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[17,6:9] <- logit.res_mornSlopexCaCo_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[18,6:9] <- logit.res_DIURconxCaCoI_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[19,6:9] <- logit.res_DIURconxCaCoII_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[20,6:9] <- logit.res_DIURconxCaCoIII_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[21,6:9] <- logit.res_DMeanconxCaCo_per[c("estimate","SE","z", "p")] ASQ_per_results_INT[22,6:9] <-logit.res_DIURSlopexCaCo_per[c("estimate","SE","z", "p")] ## problem solving skills ##### ASQ_prob_results_INT <- setNames(data.frame(matrix(ncol = 9, nrow = 22)), c("Cortisol Index", "B", "SE", #"LL", #"UL", "z", "p", "OR", "SE", #"LL", #"UL", "z", "p")) ASQ_prob_results_INT[,1] <- c("By Child Sex", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope", "By Case VS Control", "Morning Early Pregnancy", "Morning Mid Pregnancy", "Morning Late Pregnancy", "Morning Pregnancy Mean", "Mean Morning_Slope", "Diurnal Early Pregnancy", "Diurnal Mid Pregnancy", "Diurnal Late Pregnancy", "Diurnal Pregnancy Mean", "Mean Diurnal_Slope") #score #ChS ASQ_prob_results_INT[2,2:5] <- OR_ChS_morning_probI[c("estimate","SE","t", "p")] ASQ_prob_results_INT[3,2:5] <- OR_ChS_morning_probII[c("estimate","SE","t", "p")] ASQ_prob_results_INT[4,2:5] <- OR_ChS_morning_probIII[c("estimate","SE","t", "p")] ASQ_prob_results_INT[5,2:5] <- OR_ChS_MMean_prob[c("estimate","SE","t", "p")] ASQ_prob_results_INT[6,2:5] <- OR_ChS_MSlope_prob[c("estimate","SE","t", "p")] ASQ_prob_results_INT[7,2:5] <- OR_ChS_diurnal_probI[c("estimate","SE","t", "p")] ASQ_prob_results_INT[8,2:5] <- OR_ChS_diurnal_probII[c("estimate","SE","t", "p")] ASQ_prob_results_INT[9,2:5] <- OR_ChS_diurnal_probIII[c("estimate","SE","t", "p")] ASQ_prob_results_INT[10,2:5] <- OR_ChS_DMean_prob[c("estimate","SE","t", "p")] ASQ_prob_results_INT[11,2:5] <- OR_ChS_DSlope_prob[c("estimate","SE","t", "p")] #CaCo ASQ_prob_results_INT[13,2:5] <- OR_CaCo_morning_probI[c("estimate","SE","t", "p")] ASQ_prob_results_INT[14,2:5] <- OR_CaCo_morning_probII[c("estimate","SE","t", "p")] ASQ_prob_results_INT[15,2:5] <- OR_CaCo_morning_probIII[c("estimate","SE","t", "p")] ASQ_prob_results_INT[16,2:5] <- OR_CaCo_MMean_prob[c("estimate","SE","t", "p")] ASQ_prob_results_INT[17,2:5] <- OR_CaCo_MSlope_prob[c("estimate","SE","t", "p")] ASQ_prob_results_INT[18,2:5] <- OR_CaCo_diurnal_probI[c("estimate","SE","t", "p")] ASQ_prob_results_INT[19,2:5] <- OR_CaCo_diurnal_probII[c("estimate","SE","t", "p")] ASQ_prob_results_INT[20,2:5] <- OR_CaCo_diurnal_probIII[c("estimate","SE","t", "p")] ASQ_prob_results_INT[21,2:5] <- OR_CaCo_DMean_prob[c("estimate","SE","t", "p")] ASQ_prob_results_INT[22,2:5] <- OR_CaCo_DSlope_prob[c("estimate","SE","t", "p")] ##delay #ChS ASQ_prob_results_INT[2,6:9] <-logit.res_CARconxChSI_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[3,6:9] <-logit.res_CARconxChSII_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[4,6:9] <-logit.res_CARconxChSIII_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[5,6:9] <-logit.res_MeanconxChS_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[6,6:9] <-logit.res_mornSlopexChS_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[7,6:9] <-logit.res_DIURconxChSI_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[8,6:9] <-logit.res_DIURconxChSII_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[9,6:9] <-logit.res_DIURconxChSIII_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[10,6:9] <- logit.res_DMeanconxChS_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[11,6:9] <-logit.res_DIURSlopexChS_prob[c("estimate","SE","z", "p")] #CaCo ASQ_prob_results_INT[13,6:9] <- logit.res__CARconxCaCoI_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[14,6:9] <- logit.res_CARconxCaCoII_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[15,6:9] <- logit.res_CARconxCaCoIII_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[16,6:9] <- logit.res_MeanconxCaCo_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[17,6:9] <- logit.res_mornSlopexCaCo_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[18,6:9] <- logit.res_DIURconxCaCoI_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[19,6:9] <- logit.res_DIURconxCaCoII_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[20,6:9] <- logit.res_DIURconxCaCoIII_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[21,6:9] <- logit.res_DMeanconxCaCo_prob[c("estimate","SE","z", "p")] ASQ_prob_results_INT[22,6:9] <-logit.res_DIURSlopexCaCo_prob[c("estimate","SE","z", "p")] ################################################################################# #################### Sensitivity Analyses ######################### ASQ_df_final <- ASQ_df_final_sub ############################### Ordinal Regression ######################## ########################## Gross Motor Development ########################### ############## Morning Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### ## I #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 # does not converge, hence taking starting values from M1 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, data = ASQ_df_final, na.action = "na.omit", start = c(start_guess), Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ##M3 # does not converge, hence taking starting values from M1 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## by child sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_grossI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## by case VS control M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_grossI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #II #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, # control=glm.control(maxit=50), start = c(start_guess), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## by child sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_grossII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## by case VS control M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_grossII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") ## Follow-up #M2 M2_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M2_xCaCo) ctable <- coef(summary(M2_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_grossII.M2 <- ordinal_apa(M2_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #M3 M3_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M3_xCaCo) ctable <- coef(summary(M3_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_grossII.M3 <- ordinal_apa(M3_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") ## for cases M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[4,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[4,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # does not converge, hence taking starting values from M2 start_guess <- c(M2$coefficients, rep(0,2), M2$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[4,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## for controls M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 start_guess <- c(M1$coefficients, rep(0,11), M1$zeta) M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, start = c(start_guess), data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # does not converge, hence taking starting values from M1 start_guess <- c(M1$coefficients, rep(0,13), M1$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #III #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[6,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 #start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[6,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[6,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## by child sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_grossIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") ## by case VS control M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_grossIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") ## Follow-up #M2 M2_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M2_xCaCo) ctable <- coef(summary(M2_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_grossIII.M2 <- ordinal_apa(M2_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #M3 M3_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M3_xCaCo) ctable <- coef(summary(M3_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_grossIII.M3 <- ordinal_apa(M3_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") ## for cases M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[7,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[7,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # does not converge, hence taking starting values from M1 start_guess <- c(M2$coefficients, rep(0,2), M2$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[7,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## for controls M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M1) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[8,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 start_guess <- c(M1$coefficients, rep(0,11), M1$zeta) M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], start = c(start_guess), Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[8,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # # #M3 # does not converge, hence taking starting values from M1 start_guess <- c(M1$coefficients, rep(0,13), M1$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[8,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[9,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[9,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[9,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MMean_gross <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") # Follow-up M2_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M2_xCaCo) ctable <- coef(summary(M2_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MMean_gross.M2 <- ordinal_apa(M2_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") M3_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M3_xCaCo) ctable <- coef(summary(M3_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MMean_gross.M3 <- ordinal_apa(M3_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") ## for cases M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[10,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[10,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[10,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## for controls M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[11,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[11,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[11,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_gross <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MSlope_gross <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_MSlope_gross <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### ## I #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 # does not converge, hence taking starting values from M1 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, # control=glm.control(maxit=50), data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_grossI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_grossI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") ## II #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### ## III #### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[4,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[4,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[4,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_grossIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_grossIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_DMean_gross <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_DMean_gross <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) # M2 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # Diurnal_Slope + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent, # #control=glm.control(maxit=50), # data = ASQ_df_final, # start = start_guess, # Hess=TRUE) # summary(M2) # ctable <- coef(summary(M2)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M2,"Diurnal_Slope") # confint(M2)[1] # ASQ_subscale_results_diurnalR[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # M3 <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent + # Diurnal_Slope, # data = ASQ_df_final, # Hess=TRUE) # summary(M3) # ctable <- coef(summary(M3)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M3,"Diurnal_Slope") # ASQ_subscale_results_diurnalR[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### # M1_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Diurnal_Slope + # Diurnal_Slope:caseVScontrol, # data = ASQ_df_final, # Hess=TRUE) # summary(M1_xCaCo) # ctable <- coef(summary(M1_xCaCo)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # OR_CaCo_DSlope_gross <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Diurnal_Slope") # #Child_Sex #### # M1_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # Diurnal_Slope + # Diurnal_Slope:Child_Sex, # data = ASQ_df_final, # Hess=TRUE) # summary(M1_xChS) # ctable <- coef(summary(M1_xChS)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # #OR_ChS_DSlope_gross <- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ########################## Fine Motor Development ########################### ############## Morning Cortisol Concentrations and Slope ################# ### Ordinal regression by pregnancy stage #### ## I #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[13,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[13,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[13,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_fineI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_fineI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ### follow-up on child sex #### #M2 M2_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I + Child_Sex:d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M2_xChS) ctable <- coef(summary(M2_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_fineI.M2 <- ordinal_apa(M2_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") #M3 M3_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I + Child_Sex:d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M3_xChS) ctable <- coef(summary(M3_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_fineI.M3 <- ordinal_apa(M3_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## for boys M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[14,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[14,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[14,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## for girls M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[15,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[15,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[15,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ## II #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[16,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[16,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[16,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_fineII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_fineII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## III #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[17,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[17,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[17,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_fineIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_fineIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[18,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[18,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[18,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MMean_fine <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_MMean_fine <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MSlope_fine <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_MSlope_fine <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### # I #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[7,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[7,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[7,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_fineI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_fineI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") # II #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[8,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[8,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[8,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_fineII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_fineII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_II") # III #### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[9,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[9,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[9,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_fineII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_fineIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[10,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[10,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[10,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_DMean_fine <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_DMean_fine <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR[6,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, #control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M2,"Diurnal_Slope") # ASQ_subscale_results_diurnalR[6,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #res <- ordinal_apa(M3,"Diurnal_Slope") #ASQ_subscale_results_diurnalR[6,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### # M1_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Diurnal_Slope + # Diurnal_Slope:caseVScontrol, # data = ASQ_df_final, # Hess=TRUE) # summary(M1_xCaCo) # ctable <- coef(summary(M1_xCaCo)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # OR_CaCo_DSlope_fine <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Diurnal_Slope") # #Child_Sex #### # M1_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # Diurnal_Slope + # Diurnal_Slope:Child_Sex, # data = ASQ_df_final, # Hess=TRUE) # summary(M1_xChS) # ctable <- coef(summary(M1_xChS)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # OR_ChS_DSlope_fine <- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ########################## Communication skills ################## ############## Morning Cortisol Concentrations and Slope ################# ### Ordinal regression by pregnancy stage #### ## I #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[20,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, data = ASQ_df_final, na.action = "na.omit", start = start_guess, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[20,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[20,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #AppenC_OR_CaCo_morning_comI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #AppenC_OR_ChS_morning_comI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## II #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[21,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[21,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[21,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_comII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_comII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## III #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[22,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[22,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[22,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_comIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_comIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[23,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[23,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[23,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MMean_com <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_MMean_com <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[8,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[8,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[8,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MSlope_com <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_MSlope_com <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### # I #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[12,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent, # control=glm.control(maxit=50), data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[12,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[12,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_comI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #AppenC_OR_ChS_diurnal_comI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") # II #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[13,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") #ASQ_subscale_results_diurnal[13,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[13,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_comII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_comII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_II") # III #### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[14,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[14,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 start_guess <- c(M1$coefficients, rep(0,14), M1$zeta) M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III + postpartum_Cesd_cent + postpartum_BAI_cent, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") res <- c(ctable["cort_d_AUC_noCAR_III",]) AppenC_ASQ_subscale_results_diurnal[14,c(14:15, 18:19)] <- res # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_comII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_comIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[15,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[15,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[15,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_DMean_com <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_DMean_com <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR[9,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, #control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR[9,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR[9,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #AppenC_OR_CaCo_DSlope_com <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Diurnal_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #OR_ChS_DSlope_com <- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ########################## Personal Social Skills ################# ############## Morning Cortisol Concentrations and Slope ################# ### Ordinal regression by pregnancy stage #### ## I #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[25,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.omit", Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[25,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[25,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_perI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_perI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## II #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[26,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[26,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[26,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_perII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_perII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## III #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[27,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[27,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[27,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_perIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_perIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[28,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[28,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[28,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MMean_per <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_MMean_per <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[11,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[11,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[11,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MSlope_per <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_MSlope_per <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### # I #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[17,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[17,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[17,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # # Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_perI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_perI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") # II #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[18,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[18,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[18,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_perII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_perII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_II") # III #### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[19,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[19,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[19,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_perII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_perIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[20,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[20,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[20,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_DMean_per <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_DMean_per <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR[12,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 start_guess <- c(M1$coefficients, rep(0,12), M1$zeta) # M2 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # Diurnal_Slope + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent, # #control=glm.control(maxit=50), # data = ASQ_df_final, # start = start_guess, # na.action = "na.omit", # Hess=TRUE) # summary(M2) # ctable <- coef(summary(M2)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M2,"Diurnal_Slope") # ASQ_subscale_results_diurnalR[12,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 # M3 <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent + # Diurnal_Slope, # data = ASQ_df_final, # Hess=TRUE) # summary(M3) # ctable <- coef(summary(M3)) # ## calculate and store p values # p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 # ## combined table # ctable <- cbind(ctable, "p value" = p) # res <- ordinal_apa(M3,"Diurnal_Slope") # ASQ_subscale_results_diurnalR[12,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### # #caseVScontrol #### start_guess <- c(M1$coefficients, rep(0,2), M1$zeta) M1_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + caseVScontrol + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_DSlope_per <- ordinal_apa(M1_xCaCo,"Diurnal_Slope:caseVScontrolcontrol") #Child_Sex #### start_guess <- c(M1$coefficients, 0, M1$zeta) M1_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #AppenC_OR_ChS_DSlope_per<- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ########################## Problem Solving #################### ############## Morning Cortisol Concentrations and Slope ################# ### Ordinal regression by pregnancy stage #### ## I #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[30,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #class(factor(ASQ_df_final$Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T)) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.omit", Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_I") #confint(M2) AppenC_ASQ_subscale_results_morning[30,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning[30,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_probI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_I") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_probI <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_I") ## II #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[31,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[31,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning[31,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_probII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_probII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_II") ## III #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M1,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[32,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[32,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + d_AUC_CAR_only_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning[32,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) ### Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_morning_probIII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:d_AUC_CAR_only_III") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_morning_probIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:d_AUC_CAR_only_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[33,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[33,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning[33,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MMean_prob <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_MMean_prob <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_CAR_only") #### Ordinal Regression on Morning Decline ###### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[14,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[14,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Morning_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR[14,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_MSlope_prob <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:Morning_Slope") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_MSlope_prob <- ordinal_apa(M1_xChS,"Child_Sexgirl:Morning_Slope") ############## Diurnal Cortisol Concentrations and Slope ################# #### Ordinal Regression on Concentrations x pregstage ##### # I #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange, ordered=T) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[22,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[22,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal[22,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_probI <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_probI <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_I") # II #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[23,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[23,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal[23,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_probII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") #Child_Sex M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_probII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_II") # III #### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[24,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[24,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal[24,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_diurnal_probII <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #Child_Sex start_guess <- c(M1$coefficients, rep(0,1), M1$zeta) M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_diurnal_probIII <- ordinal_apa(M1_xChS,"Child_Sexgirl:cort_d_AUC_noCAR_III") #### Ordinal Regression on mean concentrations ##### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[25,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, # control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[25,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M3,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal[25,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_DMean_prob <- ordinal_apa(M1_xCaCo,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #Child_Sex #### M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_DMean_prob <- ordinal_apa(M1_xChS,"Child_Sexgirl:mean_d_AUC_DIUR_only") #### Ordinal Regression on Diurnal Decline ###### M1 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M1) ctable <- coef(summary(M1)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) ctable res <- ordinal_apa(M1,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR[15,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M2 M2 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, #control=glm.control(maxit=50), data = ASQ_df_final, Hess=TRUE) summary(M2) ctable <- coef(summary(M2)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) res <- ordinal_apa(M2,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR[15,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) #M3 M3 <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + Diurnal_Slope, data = ASQ_df_final, Hess=TRUE) summary(M3) ctable <- coef(summary(M3)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) #res <- ordinal_apa(M3,"Diurnal_Slope") #AppenC_ASQ_subscale_results_diurnalR[15,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) # Interactions #### #caseVScontrol #### start_guess <- c(M1$coefficients, rep(0,2), M1$zeta) M1_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + caseVScontrol + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xCaCo) ctable <- coef(summary(M1_xCaCo)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_CaCo_DSlope_prob <- ordinal_apa(M1_xCaCo,"Diurnal_Slope:caseVScontrolcontrol") #Child_Sex #### start_guess <- c(M1$coefficients, rep(0,1), M1$zeta) M1_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, start = start_guess, Hess=TRUE) summary(M1_xChS) ctable <- coef(summary(M1_xChS)) ## calculate and store p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 ## combined table ctable <- cbind(ctable, "p value" = p) AppenC_OR_ChS_DSlope_prob <- ordinal_apa(M1_xChS,"Child_Sexgirl:Diurnal_Slope") ############################### Logistic Regression ######################## ########################## Gross Motor Development ########################### ######### Morning Cortisol Concentrations and Slope #### #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[2,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_I,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # ## end #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSI_gross <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoI_gross <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[2,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[2,14:19] <- res # II #### #M1 M1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[3,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_I,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[3,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[3,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSII_gross <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoII_gross <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") ### follow-up on cases vs controls #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") AppenC_logit.res__CARconxCaCoII_gross.M2 <- logit_apa(M2_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results AppenC_logit.res__CARconxCaCoII_gross.M3 <- logit_apa(M3_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") ## for cases #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M1_INT2_CARcon) res <- logit_apa(M1_INT2_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[4,2:7] <- res ### follow-up on cases vs controls #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[4,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[4,14:19] <- res ## for controls #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M1_INT2_CARcon) res <- logit_apa(M1_INT2_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[5,2:7] <- res ### follow-up on cases vs controls #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[5,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[5,14:19] <- res #III #### #M1 M1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[6,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_I,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[6,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[6,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSIII_gross <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoIII_gross <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") ### follow-up on cases vs controls #### #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") AppenC_logit.res__CARconxCaCoIII_gross.M2 <- logit_apa(M2_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results AppenC_logit.res__CARconxCaCoIII_gross.M3 <- logit_apa(M3_cov_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") ## for cases #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M1_INT2_CARcon) res <- logit_apa(M1_INT2_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[7,2:7] <- res ### follow-up on cases vs controls #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[7,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[7,14:19] <- res ## for controls #ASQ and prenatal COrt*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M1_INT2_CARcon) res <- logit_apa(M1_INT2_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[8,2:7] <- res #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[8,8:13] <- res #ASQ and prenatal COrt + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[8,14:19] <- res #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[9,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) AppenC_logit.res_MeanconxChS_gross <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) AppenC_logit.res_MeanconxCaCo_gross <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") ## M2 Interaction when controlling for other covariates M2_cov_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) AppenC_logit.res_MeanconxCaCo_gross_M2 <- logit_apa(M2_cov_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M3 M3_cov_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) AppenC_logit.res_MeanconxCaCo_gross_M3 <- logit_apa(M3_cov_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") ##without interaction #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[9,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[9,14:19] <- res ## Follow-up on caseVScontrol interaction #### ##for cases M1_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M1_INT2_Meancon) res <- logit_apa(M1_INT2_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[10,2:7] <- res #M2 M2_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M2_INT2_Meancon) res <- logit_apa(M2_INT2_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[10,8:13] <- res #M3 M3_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "case",], family = "binomial") summary(M3_INT2_Meancon) res <- logit_apa(M3_INT2_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[10,14:19] <- res ##for controls M1_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M1_INT2_Meancon) res <- logit_apa(M1_INT2_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[11,2:7] <- res #M2 M2_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M2_INT2_Meancon) res <- logit_apa(M2_INT2_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[11,8:13] <- res #M3 M3_INT2_Meancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_Cesd_cent + postpartum_BAI_cent + mean_d_AUC_CAR_only, data = ASQ_df_final[ASQ_df_final$caseVScontrol == "control",], family = "binomial") summary(M3_INT2_Meancon) res <- logit_apa(M3_INT2_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[11,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[2,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) AppenC_logit.res_mornSlopexChS_gross <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) AppenC_logit.res_mornSlopexCaCo_gross <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[2,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[2,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[2,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[2,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[2,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSI_gross <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoI_gross <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # II #### #M1 M1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[3,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[3,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[3,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSII_gross <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoII_gross <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") # III #### #M1 M1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[4,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[4,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[4,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSIII_gross <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoIII_gross <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[5,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) AppenC_logit.res_DMeanconxChS_gross <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) AppenC_logit.res_DMeanconxCaCo_gross <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[5,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[5,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[3,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) AppenC_logit.res_DIURSlopexChS_gross <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) AppenC_logit.res_DIURSlopexCaCo_gross <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[3,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[3,14:19] <- res ########################## Fine Motor Development ########################### ######### Morning Cortisol Concentrations and Slope #### # assumptions ### # new_ASQ.total <- na.omit(ASQ_df_final[,-c(4, 7:18,22)]) #first exclude the pregnancy-stage specific predictors # # # Fit the logistic regression model # model <- glm(Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent, # data = new_ASQ.total, # family = binomial, # na.action = "na.exclude") # # # Predict the probability (p) of diabete positivity # probabilities <- predict(model, type = "response") # # predicted.classes <- ifelse(probabilities > 0.3, "pos", "neg") # # head(predicted.classes) # # Select only numeric predictors # mydata <- new_ASQ.total %>% # dplyr::select_if(is.numeric) # #mydata <- mydata[,-c(5:10)] # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # # ## Cook's distance # plot(model, which = 4, id.n = 3) # # #influential cases # # Extract model results # #new_ASQ <- ASQ_df_final[,-c(10:14)] #first exclude the other outcome variables # model.data <- augment(model, data = new_ASQ.total) %>% mutate(index = 1:n()) # outliers <- model.data %>% top_n(3, .cooksd) #with with neurodevelopmental delay, high depression and anxiety (two controls, one case) # # #plot standardize residuals # ggplot(model.data, aes(index, .std.resid)) + # geom_point(aes(color = Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom), alpha = .5) + # theme_bw() # model.data %>% # filter(abs(.std.resid) > 3) #no influential cases # # #multicollinearity # car::vif(model) #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[13,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[13,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[13,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSI_fine <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") ## Follow-up on sig. interaction #### #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results AppenC_logit.res_CARconxChSI_fine.M2 <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) AppenC_logit.res_CARconxChSI_fine.M3 <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") ## For boys #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M1_CARcon) res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[14,2:7] <- res #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + #Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I + d_AUC_CAR_only_II + d_AUC_CAR_only_III, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[14,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], control=glm.control(maxit=50), family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[14,14:19] <- res #predictor of interest itself leads to fitted probabilities of 0 or 1 ## For girls #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M1_CARcon) res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[15,2:7] <- res #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[15,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], control=glm.control(maxit=50), family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[15,14:19] <- res #fitted probabilities would lead to 0 or 1 # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoI_fine <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") # II #### #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[16,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[16,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[16,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSII_fine <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoII_fine <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") # III #### #M1 M1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[17,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[17,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[17,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSIII_fine <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoIII_fine <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[18,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) AppenC_logit.res_MeanconxChS_fine <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) #AppenC_logit.res_MeanconxCaCo_fine <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[18,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[18,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[5,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) AppenC_logit.res_mornSlopexChS_fine <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) AppenC_logit.res_mornSlopexCaCo__fine <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[5,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[5,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[7,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[7,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[7,14:19] <- res ### interactions ##### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSI_fine <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") ### Follow-up on child sex interaction M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) AppenC_logit.res_DIURconxChSI_fine.M2 <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) AppenC_logit.res_DIURconxChSI_fine.M3 <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") ### for boys #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M1_INT1_DIURcon) res <- logit_apa(M1_INT1_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[8,2:7] <- res #M2 M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, control=glm.control(maxit=50), data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M2_cov_DIURcon) res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[8,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], family = "binomial") summary(M3_cov_DIURcon) res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[8,14:19] <- res ### for girls #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M1_INT1_DIURcon) res <- logit_apa(M1_INT1_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[9,2:7] <- res M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M2_cov_DIURcon) res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[9,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], family = "binomial") summary(M3_cov_DIURcon) res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[9,14:19] <- res #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoI_fine <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # II #### #M1 M1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[10,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[10,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[10,14:19] <- res ### interactions ##### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSII_fine <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoII_fine <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") # III #### #M1 M1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[11,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[11,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[11,14:19] <- res ### interactions ##### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSIII_fine <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoIII_fine <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[12,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) AppenC_logit.res_DMeanconxChS_fine <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) AppenC_logit.res_DMeanconxCaCo_fine <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[12,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[12,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[6,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) AppenC_logit.res_DIURSlopexChS_fine <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) AppenC_logit.res_DIURSlopexCaCo_fine <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[6,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[6,14:19] <- res ########################## Communication skills ################## ######### Morning Cortisol Concentrations and Slope #### # assumptions ### # new_ASQ.total <- na.omit(ASQ_df_final[,-c(4, 7:18,22)]) #first exclude the pregnancy-stage specific predictors # # # Fit the logistic regression model # model <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent, # data = new_ASQ.total, # family = binomial, # na.action = "na.exclude") # # # Predict the probability (p) of diabete positivity # probabilities <- predict(model, type = "response") # # predicted.classes <- ifelse(probabilities > 0.3, "pos", "neg") # # head(predicted.classes) # # Select only numeric predictors # mydata <- new_ASQ.total %>% # dplyr::select_if(is.numeric) # #mydata <- mydata[,-c(5:10)] # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # # ## Cook's distance # plot(model, which = 4, id.n = 3) # # #influential cases # # Extract model results # #new_ASQ <- ASQ_df_final[,-c(10:14)] #first exclude the other outcome variables # model.data <- augment(model, data = new_ASQ.total) %>% mutate(index = 1:n()) # outliers <- model.data %>% top_n(3, .cooksd) #with with neurodevelopmental delay, high depression and anxiety (two controls, one case) # # #plot standardize residuals # ggplot(model.data, aes(index, .std.resid)) + # geom_point(aes(color = Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom), alpha = .5) + # theme_bw() # model.data %>% # filter(abs(.std.resid) > 3) #no influential cases # # #multicollinearity # car::vif(model) #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[20,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[20,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[20,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSI_com <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res_CARconxCaCoI_com <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") # II #### #M1 M1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[21,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[21,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[21,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSII_com <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res_CARconxCaCoII_com <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") # III #### #M1 M1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[22,2:7] <- res # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[22,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[22,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSIII_com <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res_CARconxCaCoIII_com <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[23,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) AppenC_logit.res_MeanconxChS_com <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) AppenC_logit.res_MeanconxCaCo_com <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[23,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[23,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[8,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) AppenC_logit.res_mornSlopexChS_com <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) AppenC_logit.res_mornSlopexCaCo_com <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[8,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[8,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[14,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[14,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[14,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSI_com <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoI_com <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # II #### #M1 M1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[15,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[15,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[15,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSII_com <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoII_com <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") # II #### #M1 M1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[16,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[16,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[16,14:19] <- res ## interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSIII_com <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoIII_com <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[17,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) AppenC_logit.res_DMeanconxChS_com <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) AppenC_logit.res_DMeanconxCaCo_com <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[17,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[17,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[9,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) AppenC_logit.res_DIURSlopexChS_com <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) AppenC_logit.res_DIURSlopexCaCo_com <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[9,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[9,14:19] <- res ########################## Personal Social Skills ################# ######### Morning Cortisol Concentrations and Slope #### # assumptions ### # new_ASQ.total <- na.omit(ASQ_df_final[,-c(4, 7:18,22)]) #first exclude the pregnancy-stage specific predictors # # # Fit the logistic regression model # model <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent, # data = new_ASQ.total, # family = binomial, # na.action = "na.exclude") # # # Predict the probability (p) of diabete positivity # probabilities <- predict(model, type = "response") # # predicted.classes <- ifelse(probabilities > 0.3, "pos", "neg") # # head(predicted.classes) # # Select only numeric predictors # mydata <- new_ASQ.total %>% # dplyr::select_if(is.numeric) # #mydata <- mydata[,-c(5:10)] # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # # ## Cook's distance # plot(model, which = 4, id.n = 3) # # #influential cases # # Extract model results # #new_ASQ <- ASQ_df_final[,-c(10:14)] #first exclude the other outcome variables # model.data <- augment(model, data = new_ASQ.total) %>% mutate(index = 1:n()) # outliers <- model.data %>% top_n(3, .cooksd) #with with neurodevelopmental delay, high depression and anxiety (two controls, one case) # # #plot standardize residuals # ggplot(model.data, aes(index, .std.resid)) + # geom_point(aes(color = Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom), alpha = .5) + # theme_bw() # model.data %>% # filter(abs(.std.resid) > 3) #no influential cases # # #multicollinearity # car::vif(model) #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[25,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[25,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[25,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSI_per <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoI_per <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") # II #### #M1 M1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[26,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[26,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[26,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSII_per <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoII_per <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") # III #### #M1 M1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[27,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[27,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[27,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSIII_per <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoIII_per <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[28,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) # ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) AppenC_logit.res_MeanconxChS_per <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) AppenC_logit.res_MeanconxCaCo_per <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[28,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[28,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[11,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) AppenC_logit.res_mornSlopexChS_per <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) AppenC_logit.res_mornSlopexCaCo_per <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[11,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[11,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[19,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[19,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[19,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSI_per <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoI_per <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") ## II #### #M1 M1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[20,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[20,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[20,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSII_per <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoII_per <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") ## III #### #M1 M1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[21,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[21,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[21,14:19] <- res ## interactions #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSIII_per <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoIII_per <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[22,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) AppenC_logit.res_DMeanconxChS_per <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) AppenC_logit.res_DMeanconxCaCo_per <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[22,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[22,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[12,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) AppenC_logit.res_DIURSlopexChS_per <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) AppenC_logit.res_DIURSlopexCaCo_per <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[12,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[12,14:19] <- res ########################## Problem Solving #################### ######### Morning Cortisol Concentrations and Slope #### # assumptions ### # new_ASQ.total <- na.omit(ASQ_df_final[,-c(4, 7:18,22)]) #first exclude the pregnancy-stage specific predictors # # # Fit the logistic regression model # model <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ # ChildAge_ASQ_months_allchildren_cent + # Child_Sex + # caseVScontrol + # Maternal_Age_Years_cent + # Maternal_Education + # Parity + # Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + # Weight_Gain_cent + # Maternal_Hypertensive_Disorders_anyVSnone + # Maternal_Diabetes_Disorders_anyVSnone + # Maternal_Smoking_During_Pregnancy + # Gestational_Age_Weeks_cent + # Child_Birth_Weight_cent + # postpartum_Cesd_cent + # postpartum_BAI_cent, # data = new_ASQ.total, # family = binomial, # na.action = "na.exclude") # # # Predict the probability (p) of diabete positivity # probabilities <- predict(model, type = "response") # # predicted.classes <- ifelse(probabilities > 0.3, "pos", "neg") # # head(predicted.classes) # # Select only numeric predictors # mydata <- new_ASQ.total %>% # dplyr::select_if(is.numeric) # #mydata <- mydata[,-c(5:10)] # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # #create scatterplots # ggplot(mydata, aes(logit, predictor.value))+ # geom_point(size = 0.5, alpha = 0.5) + # geom_smooth(method = "loess") + # theme_bw() + # facet_wrap(~predictors, scales = "free_y") # # ## Cook's distance # plot(model, which = 4, id.n = 3) # # #influential cases # # Extract model results # #new_ASQ <- ASQ_df_final[,-c(10:14)] #first exclude the other outcome variables # model.data <- augment(model, data = new_ASQ.total) %>% mutate(index = 1:n()) # outliers <- model.data %>% top_n(3, .cooksd) #with with neurodevelopmental delay, high depression and anxiety (two controls, one case) # # #plot standardize residuals # ggplot(model.data, aes(index, .std.resid)) + # geom_point(aes(color = Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_norm_dichom), alpha = .5) + # theme_bw() # model.data %>% # filter(abs(.std.resid) > 3) #no influential cases # # #multicollinearity # car::vif(model) #### Logistic Regression on Pregnancy CAR concentrations per pregnancy stage#### # I #### #M1 M1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[30,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[30,8:13] <- res #M3 ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_I") AppenC_ASQ_subscale_results_morning_ND[30,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_I + d_AUC_CAR_only_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSI_prob <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_I") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_I + d_AUC_CAR_only_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res__CARconxCaCoI_prob <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_I") # II #### #M1 M1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[31,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[31,8:13] <- res #M3 ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_II") AppenC_ASQ_subscale_results_morning_ND[31,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_II + d_AUC_CAR_only_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSII_prob <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_II") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_II + d_AUC_CAR_only_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res_CARconxCaCoII_prob <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_II") # III #### #M1 M1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_CARcon) #document results res <- logit_apa(M1_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[32,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_CARcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(d_AUC_CAR_only_III,d_AUC_CAR_only_II,d_AUC_CAR_only_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_CARcon) #document results res <- logit_apa(M2_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[32,8:13] <- res #M3 ASQ and prenatal Cort + postnatal covariates M3_cov_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + d_AUC_CAR_only_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_CARcon) #document results res <- logit_apa(M3_cov_CARcon,"d_AUC_CAR_only_III") AppenC_ASQ_subscale_results_morning_ND[32,14:19] <- res ### interactions #### #ASQ and prenatal Cort*child sex M1_INT1_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + d_AUC_CAR_only_III + d_AUC_CAR_only_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_CARcon) AppenC_logit.res_CARconxChSIII_prob <- logit_apa(M1_INT1_CARcon,"Child_Sexgirl:d_AUC_CAR_only_III") # ASQ and cortisol*caseVScontrol M1_INT2_CARcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + d_AUC_CAR_only_III + d_AUC_CAR_only_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_CARcon) AppenC_logit.res_CARconxCaCoIII_prob <- logit_apa(M1_INT2_CARcon,"caseVScontrolcontrol:d_AUC_CAR_only_III") #### Logistic Regression CAR mean concentrations #### #M1 M1_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_Meancon) #document results res <- logit_apa(M1_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[33,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_Meancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_CAR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_Meancon) AppenC_logit.res_MeanconxChS_prob <- logit_apa(M1_INT1_Meancon,"Child_Sexgirl:mean_d_AUC_CAR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_CAR_only + mean_d_AUC_CAR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_Meancon) AppenC_logit.res_MeanconxCaCo_prob <- logit_apa(M1_INT2_Meancon,"caseVScontrolcontrol:mean_d_AUC_CAR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_Meancon) #document results res <- logit_apa(M2_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[33,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_Meancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_CAR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_Meancon) #document results res <- logit_apa(M3_cov_Meancon,"mean_d_AUC_CAR_only") AppenC_ASQ_subscale_results_morning_ND[33,14:19] <- res #### Logistic Regression on morning slope #### #M1 M1_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_mornSlope) #document results res <- logit_apa(M1_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[14,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_mornSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Morning_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Morning_Slope + Morning_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_mornSlope) AppenC_logit.res_mornSlopexChS_prob <- logit_apa(M1_INT1_mornSlope,"Child_Sexgirl:Morning_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Morning_Slope + Morning_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_mornSlope) AppenC_logit.res_mornSlopexCaCo_prob <- logit_apa(M1_INT2_mornSlope,"caseVScontrolcontrol:Morning_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_mornSlope) #document results res <- logit_apa(M2_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[14,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_mornSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Morning_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_mornSlope) #document results res <- logit_apa(M3_cov_mornSlope,"Morning_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[14,14:19] <- res ######### Diurnal Cortisol Concentrations and Slope ####### #### Logistic Regression on Pregnancy diurnal concentrations per pregnancy stage#### # I #### #M1 M1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[24,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[24,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_I, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_I") AppenC_ASQ_subscale_results_diurnal_ND[24,14:19] <- res ### interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSI_prob <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_I") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_I + cort_d_AUC_noCAR_I:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoI_prob <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_I") # II #### #M1 M1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[25,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[25,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_II, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_II") AppenC_ASQ_subscale_results_diurnal_ND[25,14:19] <- res ### interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSII_prob <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_II") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_II + cort_d_AUC_noCAR_II:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoII_prob <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_II") # III #### #M1 M1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURcon) #document results res <- logit_apa(M1_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[26,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURcon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(cort_d_AUC_noCAR_I,cort_d_AUC_noCAR_II,cort_d_AUC_noCAR_III) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #following up on main effect: #ASQ and cortisol concentration + prenatal covariates M2_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURcon) #document results res <- logit_apa(M2_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[26,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + cort_d_AUC_noCAR_III, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURcon) #document results res <- logit_apa(M3_cov_DIURcon,"cort_d_AUC_noCAR_III") AppenC_ASQ_subscale_results_diurnal_ND[26,14:19] <- res ### interaction #### #ASQ and prenatal Cort*child sex M1_INT1_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURcon) AppenC_logit.res_DIURconxChSIII_prob <- logit_apa(M1_INT1_DIURcon,"Child_Sexgirl:cort_d_AUC_noCAR_III") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURcon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + cort_d_AUC_noCAR_III + cort_d_AUC_noCAR_III:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURcon) AppenC_logit.res_DIURconxCaCoIII_prob <- logit_apa(M1_INT2_DIURcon,"caseVScontrolcontrol:cort_d_AUC_noCAR_III") #### Logistic Regression diurnal mean concentrations #### #M1 M1_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only, na.action = "na.exclude", data = ASQ_df_final, family = "binomial") summary(M1_DMeancon) #document results res <- logit_apa(M1_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[27,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DMeancon, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(mean_d_AUC_DIUR_only) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DMeancon) AppenC_logit.res_DMeanconxChS_prob <- logit_apa(M1_INT1_DMeancon,"Child_Sexgirl:mean_d_AUC_DIUR_only") #ASQ and prenatal Cort*caseVScontrol M1_INT2_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + mean_d_AUC_DIUR_only + mean_d_AUC_DIUR_only:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DMeancon) AppenC_logit.res_DMeanconxCaCo_prob <- logit_apa(M1_INT2_DMeancon,"caseVScontrolcontrol:mean_d_AUC_DIUR_only") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M2_cov_DMeancon) #document results res <- logit_apa(M2_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[27,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DMeancon <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + mean_d_AUC_DIUR_only, data = ASQ_df_final, family = "binomial") summary(M3_cov_DMeancon) #document results res <- logit_apa(M3_cov_DMeancon,"mean_d_AUC_DIUR_only") AppenC_ASQ_subscale_results_diurnal_ND[27,14:19] <- res #### Logistic Regression on diurnal slope #### #M1 M1_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope, data = ASQ_df_final, na.action = "na.exclude", family = "binomial") summary(M1_DIURSlope) #document results res <- logit_apa(M1_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[15,2:7] <- res # # assumptions # # Predict the probability (p) # probabilities <- predict(M1_DIURSlope, type = "response") # mydata <- ASQ_df_final %>% # dplyr::select(Diurnal_Slope) # predictors <- colnames(mydata) # # Bind the logit and tidying the data for plot # mydata <- mydata %>% # mutate(logit = log(probabilities/(1-probabilities))) %>% # gather(key = "predictors", value = "predictor.value", -logit) # # plot(mydata$logit,mydata$predictor.value) ## end #ASQ and prenatal Cort*child sex M1_INT1_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + Diurnal_Slope + Diurnal_Slope:Child_Sex, data = ASQ_df_final, family = "binomial") summary(M1_INT1_DIURSlope) AppenC_logit.res_DIURSlopexChS_prob <- logit_apa(M1_INT1_DIURSlope,"Child_Sexgirl:Diurnal_Slope") #ASQ and prenatal cort*caseVScontrol M1_INT2_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Diurnal_Slope + Diurnal_Slope:caseVScontrol, data = ASQ_df_final, family = "binomial") summary(M1_INT2_DIURSlope) AppenC_logit.res_DIURSlopexCaCo_prob <- logit_apa(M1_INT2_DIURSlope,"caseVScontrolcontrol:Diurnal_Slope") #M2 ASQ and cortisol concentration + prenatal covariates M2_cov_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M2_cov_DIURSlope) #document results res <- logit_apa(M2_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[15,8:13] <- res #ASQ and prenatal Cort + postnatal covariates M3_cov_DIURSlope <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ ChildAge_ASQ_months_allchildren_cent + Child_Sex + caseVScontrol + Maternal_Age_Years_cent + Maternal_Education + Parity + Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + Weight_Gain_cent + Maternal_Hypertensive_Disorders_anyVSnone + Maternal_Diabetes_Disorders_anyVSnone + Maternal_Smoking_During_Pregnancy + Gestational_Age_Weeks_cent + Child_Birth_Weight_cent + postpartum_BAI_cent + postpartum_Cesd_cent + Diurnal_Slope, data = ASQ_df_final, family = "binomial") summary(M3_cov_DIURSlope) #document results res <- logit_apa(M3_cov_DIURSlope,"Diurnal_Slope") AppenC_ASQ_subscale_results_diurnalR_ND[15,14:19] <- res