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Master-Thesis-Analyses/AntepartumDepression_ASQ_Subscales.R
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| ### Results of prenatal depression and ASQ subscale development | |
| #01.07.2021 | |
| ### 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(rcompanion) | |
| library(tidyverse) | |
| library(broom) | |
| require(foreign) | |
| require(ggplot2) | |
| require(MASS) | |
| require(Hmisc) | |
| library(APAstyler) | |
| ## 1. cortisol data #### | |
| load("Rdata/ITU_combined_cortisol_dates_times_wide_format.Rdata") | |
| cort_IDs <- unique(wide_cort$participantID) | |
| ## 2. maternal well-being during pregnancy #### | |
| load("Rdata/processed_wellbeingduringpreg_completevars.Rdata") | |
| #for now only sleep, anxiety and CESD are included | |
| cols <- c("1", "2",grep("gestage", names(q_data_complete)), | |
| grep("GWdiff_cort_qclosest", names(q_data_complete)), | |
| grep("Cesd", names(q_data_complete)), | |
| #grep("puqe", names(q_data_complete)), | |
| grep("PSQI", names(q_data_complete)), | |
| #grep("ESS", names(q_data_complete)), | |
| grep("BAI",names(q_data_complete))) | |
| q_data_sub <- q_data_complete[,c(as.numeric(cols))] | |
| ## Depressive Symptoms Severity | |
| q_data_sub$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$participantID == ID, c("pregstage")]) | |
| #print(length(trims)) | |
| if(length(trims) >= 2){ | |
| at_least_two_assessments <- append(at_least_two_assessments, ID) | |
| } | |
| } | |
| ### Data in wide format | |
| q_data_sub_final <- q_data_subx %>% | |
| tidyr::pivot_wider( | |
| id_cols = c(participantID, | |
| pregstage, | |
| Cesd_qclosest_to_cortGW_pregMean, | |
| Depressive_Symptom_Severity, | |
| Depressive_Symptom_Severity_num, | |
| BAI_qclosest_to_cortGW_pregMean, | |
| PSQI_qclosest_to_cortGW_pregMean, | |
| additive_clinD), | |
| names_from = c(pregstage), # Can accommodate more variables, if needed. | |
| values_from = c(8:11) | |
| ) | |
| # #only overall questionnaire score during pregnancy | |
| # q_data_sub_final <- q_data_sub[,c("participantID", | |
| # "Cesd_qclosest_to_cortGW_pregMean", | |
| # "BAI_qclosest_to_cortGW_pregMean", | |
| # "PSQI_qclosest_to_cortGW_pregMean", | |
| # #"WS_CESD_variation_mean", | |
| # "Depressive_Symptom_Severity", | |
| # "Depressive_Symptom_Severity_num", | |
| # "Anxiety_Symptom_Severity", | |
| # "psych_distress")] | |
| # q_data_sub_final <- subset(q_data_sub_final, !duplicated(q_data_sub_final)) | |
| # summary(factor(q_data_sub_final$Anxiety_Symptom_Severity)) #n=5, these also have clin depression | |
| # summary(factor(q_data_sub_final$psych_distress)) | |
| # summary(factor(q_data_sub_final$Depressive_Symptom_Severity)) | |
| # 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$Maternal_Age_Years, scale = F)) | |
| regis_final <- register_data[,c("participantID", | |
| "caseVScontrol", | |
| "Parity", | |
| "Maternal_Smoking_During_Pregnancy", | |
| "Maternal_Corticosteroid_Treatment_during_Pregnancy", | |
| "Maternal_Body_Mass_Index_in_Early_Pregnancy_cent", | |
| "Child_Sex" , | |
| "Gestational_Age_Weeks_cent", | |
| "Child_Birth_Weight_cent", | |
| "Weight_Gain_cent", | |
| "Maternal_Hypertensive_Disorders_anyVSnone", | |
| "Maternal_Diabetes_Disorders_anyVSnone", | |
| "Maternal_Age_Years_cent" | |
| )] | |
| ## 4.1 medication data #### | |
| medication <- read.delim("Rdata/ITU_psychotrophicmedication05July21_Maternal_CurrentPregnancy.dat") | |
| names(medication)[1] <- "participantID" | |
| medication$ITUbroadpsychiatricmedication_18_KELA <- factor(medication$ITUbroadpsychiatricmedication_18_KELA, | |
| levels = c(0,1), | |
| labels = c("no", "yes")) | |
| ## 5. ASQ data #### | |
| ASQ <- read.csv2("Rdata/ASQ_dataset_ITU_01122020r_shorter.csv") | |
| names(ASQ)[1] <- "participantID" | |
| #extract only participants who have cortisol data and finalagerange scores | |
| #ASQ_final <- ASQ[ASQ$participantID %in% cort_IDs,c(1,3, 18:23)] | |
| ASQ_final <- ASQ[ASQ$participantID %in% cort_IDs,c(1,3,18:22)] | |
| ASQ_final$Child_ASQ_grossmotor_development_infancy_sum_finalagerange <- | |
| as.numeric(ASQ_final$Child_ASQ_grossmotor_development_infancy_sum_finalagerange) | |
| #normalized rank scores | |
| for(col in 3:ncol(ASQ_final)){ | |
| column <- ASQ_final[col] | |
| name <- paste0(names(ASQ_final[col]), "_norm") | |
| ASQ_final[name] <- blom(column, method = "rankit") | |
| } | |
| #dichotomize normalized rank scores | |
| for(col in 8:ncol(ASQ_final)){ | |
| column <- ASQ_final[col] | |
| old_name <- names(ASQ_final[col]) | |
| new_name <- paste0(names(ASQ_final[col]), "_dichom") | |
| ASQ_final[new_name] <- NA | |
| for(i in 1:nrow(ASQ_final)){ | |
| ASQ_score <- ASQ_final[[i, c(old_name)]] | |
| if(!is.na(ASQ_score)){ | |
| if(ASQ_score <= -1){ | |
| ASQ_final[[i,c(new_name)]] <- 1 | |
| } | |
| if(ASQ_score > -1){ | |
| ASQ_final[[i,c(new_name)]] <- 0 | |
| } | |
| } | |
| } | |
| } | |
| ASQ_final$ChildAge_ASQ_months_allchildren_cent <- | |
| c(scale(ASQ_final$ChildAge_ASQ_months_allchildren, scale = F)) | |
| ## 6. On Pregnancy Averages: merge all data #### | |
| #use all the ASQ data | |
| ASQ_df1 <- left_join(ASQ_final, q_data_sub_final) | |
| ASQ_df2 <- left_join(ASQ_df1, maternal_edu) | |
| ASQ_df3 <- left_join(ASQ_df2, postpartum_df_final) | |
| ASQ_df_final <- left_join(ASQ_df3, regis_final) | |
| ASQ_df_final <- left_join(ASQ_df_final, | |
| medication[,c("participantID", | |
| "ITUbroadpsychiatricmedication_18_KELA", | |
| "antidepressants_18_KELA")], | |
| copy=TRUE) | |
| ##additive pre/post effects | |
| ASQ_df_final$Pre.Post_clinD <- NA | |
| for(i in 1:nrow(ASQ_df_final)){ | |
| pre <- ASQ_df_final$Depressive_Symptom_Severity_num[[i]] | |
| post <- ASQ_df_final$postpartum_Depressive_Symptom_Severity_num[[i]] | |
| if(!is.na(pre) & !is.na(post) & pre == 0 & post == 0){ | |
| ASQ_df_final$Pre.Post_clinD[[i]] <- "never" | |
| } | |
| if(sum(pre, post, na.rm=T) == 1 & !is.na(pre) & pre == 1){ | |
| ASQ_df_final$Pre.Post_clinD[[i]] <- "pre_only" | |
| } | |
| if(sum(pre, post, na.rm=T) == 1 & !is.na(post) & post == 1){ | |
| ASQ_df_final$Pre.Post_clinD[[i]] <- "post_only" | |
| } | |
| if(sum(pre, post, na.rm=T) == 2){ | |
| ASQ_df_final$Pre.Post_clinD[[i]] <- "pre_post" | |
| } | |
| } | |
| ASQ_df_final$Pre.Post_clinD <- factor(ASQ_df_final$Pre.Post_clinD) | |
| #view(ASQ_df_final[,c("Depressive_Symptom_Severity_num", "postpartum_Depressive_Symptom_Severity_num", "Pre.Post_clinD")]) | |
| ## 7. Apply Exclusion Criteria #### | |
| ASQ_df_final <- subset(ASQ_df_final, !(Maternal_Corticosteroid_Treatment_during_Pregnancy == "yes")) | |
| #final n = 528 | |
| # ASQ_numbers <- ASQ_df_final[, c("Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom", | |
| # "Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom", | |
| # "Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom", | |
| # "Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom", | |
| # "Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom", | |
| # "Depressive_Symptom_Severity")] | |
| # | |
| # tbl_summary(data = ASQ_numbers, | |
| # by = Depressive_Symptom_Severity) | |
| #tidy up | |
| rm(list= ls()[!(ls() %in% c("ASQ_df_final", "at_least_two_assessments"))]) | |
| ASQ_df_final_sub <- ASQ_df_final[!(ASQ_df_final$ITUbroadpsychiatricmedication_18_KELA == "yes"),] | |
| ################################################################################ | |
| ## Set up results table #### | |
| ASQ_subscale_results <- setNames(data.frame(matrix(ncol = 19, nrow = 7)), | |
| c("Developmental domain", | |
| "B", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "t", | |
| "p", | |
| "B", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "t", | |
| "p", | |
| "B", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "t", | |
| "p")) | |
| ASQ_subscale_results[,1] <- c("Gross_Motor_Skills", | |
| ".. in boys", | |
| ".. in girls", | |
| "Fine_Motor_Skills", | |
| "Communication_Skills", | |
| "Personal_Social_Skills", | |
| "Problem_Solving_Skills") | |
| ##for neurodevelopmental delay | |
| ASQ_subscale_results_ND <- setNames(data.frame(matrix(ncol = 19, nrow = 7)), | |
| c("Developmental domain", | |
| "OR", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "z", | |
| "p", | |
| "OR", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "z", | |
| "p", | |
| "OR", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "z", | |
| "p")) | |
| ASQ_subscale_results_ND[,1] <- c( | |
| "Gross_Motor_Skills", | |
| ".. in boys", | |
| ".. in girls", | |
| "Fine_Motor_Skills", | |
| "Communication_Skills", | |
| "Personal_Social_Skills", | |
| "Problem_Solving_Skills") | |
| ## 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(p > 0 & p < 1){ | |
| 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 <- format(round(coef(summary(m))[x,4],3)) | |
| if(p > 0 & p < 1){ | |
| p <- snip(as.numeric(p), 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 | |
| } | |
| ################################################################################ | |
| ################### Ordered Logistic Regression ######## | |
| ### Gross Motor Development #### | |
| M.Gross <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Gross) | |
| ctable <- coef(summary(M.Gross)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Gross,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[1,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ## add covariates M2 | |
| M2.Gross <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.Gross) | |
| ctable <- coef(summary(M2.Gross)) | |
| ## calculate and 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.Gross,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[1,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ## add covariates M3 | |
| M3.Gross <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.Gross) | |
| ctable <- coef(summary(M3.Gross)) | |
| ## calculate and 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.Gross,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[1,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ## repeat without smokers | |
| M2.GrossB <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[!ASQ_df_final$Maternal_Smoking_During_Pregnancy == "yes",], | |
| Hess=TRUE) | |
| summary(M2.GrossB) | |
| ctable <- coef(summary(M2.GrossB)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| OR2_gross_noSmokers <- ordinal_apa(M2.GrossB,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ## add covariates M3 | |
| M3.GrossB <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[!ASQ_df_final$Maternal_Smoking_During_Pregnancy == "yes",], | |
| Hess=TRUE) | |
| summary(M3.GrossB) | |
| ctable <- coef(summary(M3.GrossB)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| OR3_gross_noSmokers <- ordinal_apa(M3.GrossB,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ### Interactions #### | |
| ## case vS control | |
| M.Gross_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Gross_xCaCo) | |
| ctable <- coef(summary(M.Gross_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_gross_CaCo <- ordinal_apa(M.Gross_xCaCo,"caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #Child_Sex | |
| M.Gross_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Gross_xChS) | |
| ctable <- coef(summary(M.Gross_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_gross_ChS <- ordinal_apa(M.Gross_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #add covariates | |
| M2.Gross_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| Hess=TRUE) | |
| summary(M2.Gross_xChS) | |
| ctable <- coef(summary(M2.Gross_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| OR2_gross_ChS <- ordinal_apa(M2.Gross_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #add covariates | |
| M3.Gross_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| Hess=TRUE) | |
| summary(M3.Gross_xChS) | |
| ctable <- coef(summary(M3.Gross_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| OR3_gross_ChS <- ordinal_apa(M3.Gross_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ## boys only #### | |
| M.Gross_xChS_boys <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| Hess=TRUE) | |
| summary(M.Gross_xChS_boys) | |
| ctable <- coef(summary(M.Gross_xChS_boys)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Gross_xChS_boys,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ##model 2 | |
| M2.Gross_xChS_boys <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| Hess=TRUE) | |
| summary(M2.Gross_xChS_boys) | |
| ctable <- coef(summary(M2.Gross_xChS_boys)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M2.Gross_xChS_boys,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ##Model 3 | |
| M3.Gross_xChS_boys <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + | |
| Weight_Gain_cent + | |
| Maternal_Hypertensive_Disorders_anyVSnone + | |
| Maternal_Diabetes_Disorders_anyVSnone + | |
| Maternal_Smoking_During_Pregnancy + | |
| Gestational_Age_Weeks_cent + | |
| Child_Birth_Weight_cent + | |
| postpartum_Cesd_cent + | |
| postpartum_BAI_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| Hess=TRUE) | |
| summary(M3.Gross_xChS_boys) | |
| ctable <- coef(summary(M3.Gross_xChS_boys)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| OR3_gross_ChS_boys <- ordinal_apa(M3.Gross_xChS_boys,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### girls only #### | |
| M.Gross_xChS_girls <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| Hess=TRUE) | |
| summary(M.Gross_xChS_girls) | |
| ctable <- coef(summary(M.Gross_xChS_girls)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Gross_xChS_girls,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #model 2 | |
| M2.Gross_xChS_girls <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + | |
| Weight_Gain_cent + | |
| Maternal_Hypertensive_Disorders_anyVSnone + | |
| Maternal_Diabetes_Disorders_anyVSnone + | |
| Maternal_Smoking_During_Pregnancy + | |
| Gestational_Age_Weeks_cent + | |
| Child_Birth_Weight_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| Hess=TRUE) | |
| summary(M2.Gross_xChS_girls) | |
| ctable <- coef(summary(M2.Gross_xChS_girls)) | |
| ## calculate and 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.Gross_xChS_girls,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #model3 | |
| M3.Gross_xChS_girls <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| Hess=TRUE) | |
| summary(M3.Gross_xChS_girls) | |
| ctable <- coef(summary(M3.Gross_xChS_girls)) | |
| ## calculate and 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.Gross_xChS_girls,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Fine Motor Development #### | |
| M.Fine <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Fine) | |
| ctable <- coef(summary(M.Fine)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Fine,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[4,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M2 | |
| M2.Fine <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.Fine) | |
| ctable <- coef(summary(M2.Fine)) | |
| ## calculate and 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.Fine,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[4,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M3 | |
| M3.Fine <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.Fine) | |
| ctable <- coef(summary(M3.Fine)) | |
| ## calculate and 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.Fine,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[4,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Interactions #### | |
| M.Fine_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Fine_xCaCo) | |
| ctable <- coef(summary(M.Fine_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_fine_CaCo <- ordinal_apa(M.Fine_xCaCo,"caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #Child_Sex | |
| M.Fine_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Fine_xChS) | |
| ctable <- coef(summary(M.Fine_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_fine_ChS <- ordinal_apa(M.Fine_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ### Communication Development #### | |
| M.Com <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Com) | |
| ctable <- coef(summary(M.Com)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Com,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ## M2 | |
| M2.Com <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.Com) | |
| ctable <- coef(summary(M2.Com)) | |
| ## calculate and 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.Com,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ##M3 | |
| M3.Com <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.Com) | |
| ctable <- coef(summary(M3.Com)) | |
| ## calculate and 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.Com,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Interactions #### | |
| #caseVScontrol | |
| M.Com_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Com_xCaCo) | |
| ctable <- coef(summary(M.Com_xCaCo)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Com_xCaCo,"Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrolcontrol") | |
| OR_com_CaCo <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #Child Sex | |
| M.Com_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Com_xChS) | |
| ctable <- coef(summary(M.Com_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Com_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| OR_com_ChS <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Personal/Social Development #### | |
| M.per <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.per) | |
| ctable <- coef(summary(M.per)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.per,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[6,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M2 | |
| M2.per <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.per) | |
| ctable <- coef(summary(M2.per)) | |
| ## calculate and 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.per,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[6,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M3 | |
| M3.per <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.per) | |
| ctable <- coef(summary(M3.per)) | |
| ## calculate and 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.per,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[6,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Interactions #### | |
| #caseVScontrol | |
| M.per_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.per_xCaCo) | |
| ctable <- coef(summary(M.per_xCaCo)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.per_xCaCo,"Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrolcontrol") | |
| OR_per_CaCo <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #Child Sex | |
| M.per_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.per_xChS) | |
| ctable <- coef(summary(M.per_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.per_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| OR_per_ChS <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Prob Development #### | |
| M.prob <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.prob) | |
| ctable <- coef(summary(M.prob)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.prob,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[7,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M2 | |
| M2.prob <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.prob) | |
| ctable <- coef(summary(M2.prob)) | |
| ## calculate and 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.prob,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[7,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M3 | |
| M3.prob <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.prob) | |
| ctable <- coef(summary(M3.prob)) | |
| ## calculate and 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.prob,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results[7,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Interactions #### | |
| #caseVScontrol | |
| M.prob_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.prob_xCaCo) | |
| ctable <- coef(summary(M.prob_xCaCo)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.prob_xCaCo,"Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrolcontrol") | |
| OR_prob_CaCo <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #Child Sex | |
| M.prob_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.prob_xChS) | |
| ctable <- coef(summary(M.prob_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.prob_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| OR_prob_ChS <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ################################################################################ | |
| ############### Logit ##################### | |
| # ####### Gross motor development score #### | |
| # ### assumptions #### | |
| # new_ASQ <- na.omit(ASQ_df_final[,-c(2:14,16,19:35,39:42,46:49)]) #first exclude the other outcome variables | |
| # | |
| # # Fit the logistic regression model | |
| # model <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| # ChildAge_ASQ_months_allchildren_cent + | |
| # Child_Sex + | |
| # Cesd_qclosest_to_cortGW_pregMean_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, | |
| # data = new_ASQ, | |
| # 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 %>% | |
| # 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 = 4) | |
| # | |
| # #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) %>% mutate(index = 1:n()) | |
| # model.data %>% top_n(4, .cooksd) | |
| # | |
| # #plot standardize residuls | |
| # ggplot(model.data, aes(index, .std.resid)) + | |
| # geom_point(aes(color = Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom), alpha = .5) + | |
| # theme_bw() | |
| # model.data %>% | |
| # filter(abs(.std.resid) > 3) #no influential cases | |
| # | |
| # #multicollinearity | |
| # car::vif(model) | |
| # | |
| # ### Prenatal Cesd #### | |
| #ASQ and prenatal Cesd | |
| M1_gross <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_gross) | |
| res <- logit_apa(M1_gross, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[1,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M2 | |
| M2_gross.cov <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_gross.cov) | |
| res <- logit_apa(M2_gross.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[1,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd + postnatal covariates | |
| M3_gross.cov <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_gross.cov) | |
| res <- logit_apa(M3_gross.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[1,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| ##ASQ and prenatal Cesd*child sex | |
| M1_gross.INT1 <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M1_gross.INT1) | |
| LOG_gross_xChS <- logit_apa(M1_gross.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ##M2 | |
| M2_gross.INT1 <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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.exclude", | |
| family = "binomial") | |
| summary(M2_gross.INT1) | |
| LOG2_gross_xChS <- logit_apa(M2_gross.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #M3 | |
| M3_gross.INT1 <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| data = ASQ_df_final, | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M3_gross.INT1) | |
| LOG3_gross_xChS <- logit_apa(M3_gross.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ## for boys | |
| M1_gross.INT1_boys <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M1_gross.INT1_boys) | |
| res <- logit_apa(M1_gross.INT1_boys, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ##M2 | |
| M2_gross.INT1_boys <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M2_gross.INT1_boys) | |
| res <- logit_apa(M2_gross.INT1_boys, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M3 | |
| M3_gross.INT1_boys <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + | |
| Weight_Gain_cent + | |
| Maternal_Hypertensive_Disorders_anyVSnone + | |
| Maternal_Diabetes_Disorders_anyVSnone + | |
| Maternal_Smoking_During_Pregnancy + | |
| Gestational_Age_Weeks_cent + | |
| Child_Birth_Weight_cent + | |
| postpartum_BAI_cent + | |
| postpartum_Cesd_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M3_gross.INT1_boys) | |
| res <- logit_apa(M3_gross.INT1_boys, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ## for girls | |
| M1_gross.INT1_girls <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M1_gross.INT1_girls) | |
| res <- logit_apa(M1_gross.INT1_girls, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ##M2 | |
| M2_gross.INT1_girls <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + | |
| Weight_Gain_cent + | |
| Maternal_Hypertensive_Disorders_anyVSnone + | |
| Maternal_Diabetes_Disorders_anyVSnone + | |
| Maternal_Smoking_During_Pregnancy + | |
| Gestational_Age_Weeks_cent + | |
| Child_Birth_Weight_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M2_gross.INT1_girls) | |
| res <- logit_apa(M2_gross.INT1_girls, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M3 | |
| M3_gross.INT1_girls <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M3_gross.INT1_girls) | |
| res <- logit_apa(M3_gross.INT1_girls, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_gross.INT2 <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_gross.INT2) | |
| LOG_gross_xCaCo <- logit_apa(M1_gross.INT2, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # ### Categorical Cesd #### | |
| #ASQ and prenatal Cesd | |
| M1_gross_cat <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Depressive_Symptom_Severity, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_gross_cat) | |
| #res <- logit_apa(M1_gross_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[10,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M2 | |
| M2_gross.cov_cat <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Depressive_Symptom_Severity + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| family = "binomial") | |
| summary(M2_gross.cov_cat) | |
| #res <- logit_apa(M2_gross.cov_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[10,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd + postnatal covariates | |
| M3_gross.cov_cat <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Depressive_Symptom_Severity + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_gross.cov_cat) | |
| #res <- logit_apa(M3_gross.cov_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[10,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # ####### Fine motor development score #### | |
| # ### assumptions #### | |
| new_ASQ.fine <- na.omit(ASQ_df_final[,-c(2:13,15,16,19:35,39:42,46:49)]) | |
| #first exclude the other outcome variables | |
| # # Fit the logistic regression model | |
| # model <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| # ChildAge_ASQ_months_allchildren_cent + | |
| # Child_Sex + | |
| # Cesd_qclosest_to_cortGW_pregMean_cent + | |
| # #BAI_qclosest_to_cortGW_pregMean_cent + | |
| # #PSQI_qclosest_to_cortGW_pregMean_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, | |
| # data = new_ASQ.fine, | |
| # 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.fine %>% | |
| # 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 = 4) | |
| # | |
| # #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.fine) %>% mutate(index = 1:n()) | |
| # model.data %>% top_n(4, .cooksd) | |
| # | |
| # #plot standardize residuls | |
| # ggplot(model.data, aes(index, .std.resid)) + | |
| # geom_point(aes(color = Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom), alpha = .5) + | |
| # theme_bw() | |
| # model.data %>% | |
| # filter(abs(.std.resid) > 3) #no influential cases | |
| # | |
| # #multicollinearity | |
| # car::vif(model) | |
| # ### Prenatal Cesd #### | |
| #ASQ and prenatal Cesd | |
| M1_fine <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_fine) | |
| res <- logit_apa(M1_fine, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[4,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M2 | |
| #ASQ and prenatal Cesd + prenatal covariates | |
| M2_fine.cov <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_fine.cov) | |
| res <- logit_apa(M2_fine.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[4,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + postnatal covariates | |
| M3_fine.cov <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_fine.cov) | |
| res <- logit_apa(M3_fine.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[4,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| # #ASQ and prenatal Cesd*child sex | |
| M1_fine.INT1 <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_fine.INT1) | |
| LOG_fine_xChS <- logit_apa(M1_fine.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_fine.INT2 <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_fine.INT2) | |
| LOG_fine_xCaCo <- logit_apa(M1_fine.INT2, "caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # ### Categorical Cesd #### | |
| #ASQ and prenatal Cesd | |
| M1_fine_cat <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Depressive_Symptom_Severity, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_fine_cat) | |
| #res <- logit_apa(M1_fine_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[11,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M2 | |
| M2_fine.cov_cat <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Depressive_Symptom_Severity + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| family = "binomial") | |
| summary(M2_fine.cov_cat) | |
| #res <- logit_apa(M2_fine.cov_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[11,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd + postnatal covariates | |
| M3_fine.cov_cat <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Depressive_Symptom_Severity + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_fine.cov_cat) | |
| #res <- logit_apa(M3_fine.cov_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[11,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # ####### communication development score #### | |
| # ### assumptions #### | |
| # new_ASQ.com <- na.omit(ASQ_df_final[,-c(2:13:16,19:36,38:43,47:50)]) #first exclude the other outcome variables | |
| # | |
| # # Fit the logistic regression model | |
| # model <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| # ChildAge_ASQ_months_allchildren_cent + | |
| # Child_Sex + | |
| # Cesd_qclosest_to_cortGW_pregMean_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, | |
| # data = new_ASQ.com, | |
| # 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.com %>% | |
| # 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 = 4) | |
| # | |
| # #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.com) %>% mutate(index = 1:n()) | |
| # model.data %>% top_n(4, .cooksd) | |
| # | |
| # #plot standardize residuls | |
| # ggplot(model.data, aes(index, .std.resid)) + | |
| # geom_point(aes(color = Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom), alpha = .5) + | |
| # theme_bw() | |
| # model.data %>% | |
| # filter(abs(.std.resid) > 3) #no influential cases | |
| # | |
| # #multicollinearity | |
| # car::vif(model) | |
| # ### Prenatal Cesd #### | |
| # #ASQ and prenatal Cesd | |
| M1_com <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_com) | |
| res <- logit_apa(M1_com, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + prenatal covariates | |
| M2_com.cov <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_com.cov) | |
| res <- logit_apa(M2_com.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + postnatal covariates | |
| M3_com.cov <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_com.cov) | |
| res <- logit_apa(M3_com.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| # #ASQ and prenatal Cesd*child sex | |
| M1_com.INT1 <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_com.INT1) | |
| LOG_com_xChS <- logit_apa(M1_com.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_com.INT2 <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_com.INT2) | |
| LOG_com_xCaCo <- logit_apa(M1_com.INT2, "caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # ####### personal/social development score #### | |
| # ### assumptions #### | |
| # new_ASQ.per <- na.omit(ASQ_df_final[,-c(2:12, 14:16,19:36,38:43,47:50)]) #first exclude the other outcome variables | |
| # | |
| # # Fit the logistic regression model | |
| # model <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| # ChildAge_ASQ_months_allchildren_cent + | |
| # Child_Sex + | |
| # Cesd_qclosest_to_cortGW_pregMean_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, | |
| # data = new_ASQ.per, | |
| # 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.per %>% | |
| # 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 = 4) | |
| # | |
| # #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.per) %>% mutate(index = 1:n()) | |
| # model.data %>% top_n(4, .cooksd) | |
| # | |
| # #plot standardize residuls | |
| # ggplot(model.data, aes(index, .std.resid)) + | |
| # geom_point(aes(color = Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom), alpha = .5) + | |
| # theme_bw() | |
| # model.data %>% | |
| # filter(abs(.std.resid) > 3) #no influential cases | |
| # | |
| # #multicollinearity | |
| # car::vif(model) | |
| # | |
| # ### Prenatal Cesd #### | |
| # #ASQ and prenatal Cesd | |
| M1_per <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_per) | |
| res <- logit_apa(M1_per, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[6,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + prenatal covariates | |
| M2_per.cov <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_per.cov) | |
| res <- logit_apa(M2_per.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[6,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd + postnatal covariates | |
| M3_per.cov <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_per.cov) | |
| res <- logit_apa(M3_per.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[6,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| # #ASQ and prenatal Cesd*child sex | |
| M1_per.INT1 <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_per.INT1) | |
| LOG_per_xChS <- logit_apa(M1_per.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_per.INT2 <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_per.INT2) | |
| LOG_per_xCaCo <- logit_apa(M1_per.INT2, "caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # ####### problem solving development score #### | |
| # ### assumptions #### | |
| # new_ASQ.prob <- na.omit(ASQ_df_final[,-c(2:13, 15:16,19:36,38:43,47:50)]) #first exclude the other outcome variables | |
| # | |
| # # Fit the logistic regression model | |
| # model <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| # ChildAge_ASQ_months_allchildren_cent + | |
| # Child_Sex + | |
| # Cesd_qclosest_to_cortGW_pregMean_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, | |
| # data = new_ASQ.prob, | |
| # 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.prob %>% | |
| # 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 = 4) | |
| # | |
| # #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.prob) %>% mutate(index = 1:n()) | |
| # model.data %>% top_n(4, .cooksd) | |
| # | |
| # #plot standardize residuls | |
| # ggplot(model.data, aes(index, .std.resid)) + | |
| # geom_point(aes(color = Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom), alpha = .5) + | |
| # theme_bw() | |
| # model.data %>% | |
| # filter(abs(.std.resid) > 3) #no influential cases | |
| # | |
| # #multicollinearity | |
| # car::vif(model) | |
| # ### Prenatal Cesd #### | |
| # #ASQ and prenatal Cesd | |
| M1_prob <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_prob) | |
| res <- logit_apa(M1_prob, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[7,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + prenatal covariates | |
| M2_prob.cov <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_prob.cov) | |
| res <- logit_apa(M2_prob.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[7,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + postnatal covariates | |
| M3_prob.cov <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_prob.cov) | |
| res <- logit_apa(M3_prob.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ASQ_subscale_results_ND[7,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| # #ASQ and prenatal Cesd*child sex | |
| M1_prob.INT1 <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_prob.INT1) | |
| LOG_prob_xChS <- logit_apa(M1_prob.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_prob.INT2 <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_prob.INT2) | |
| LOG_prob_xCaCo <- logit_apa(M1_prob.INT2, "caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ######################### Sensitivity analysis ################### | |
| ## Set up results table #### | |
| Appen_ASQ_subscale_results <- setNames(data.frame(matrix(ncol = 19, nrow = 7)), | |
| c("Developmental domain", | |
| "B", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "t", | |
| "p", | |
| "B", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "t", | |
| "p", | |
| "B", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "t", | |
| "p")) | |
| Appen_ASQ_subscale_results[,1] <- c("Gross_Motor_Skills", | |
| ".. in boys", | |
| ".. in girls", | |
| "Fine_Motor_Skills", | |
| "Communication_Skills", | |
| "Personal_Social_Skills", | |
| "Problem_Solving_Skills") | |
| ##for neurodevelopmental delay | |
| Appen_ASQ_subscale_results_ND <- setNames(data.frame(matrix(ncol = 19, nrow = 7)), | |
| c("Developmental domain", | |
| "OR", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "z", | |
| "p", | |
| "OR", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "z", | |
| "p", | |
| "OR", | |
| "SE", | |
| "LL", | |
| "UL", | |
| "z", | |
| "p")) | |
| Appen_ASQ_subscale_results_ND[,1] <- c( | |
| "Gross_Motor_Skills", | |
| ".. in boys", | |
| ".. in girls", | |
| "Fine_Motor_Skills", | |
| "Communication_Skills", | |
| "Personal_Social_Skills", | |
| "Problem_Solving_Skills") | |
| ASQ_df_final <- ASQ_df_final_sub | |
| ################### Ordered Logistic Regression ######## | |
| ### Gross Motor Development #### | |
| M.Gross <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Gross) | |
| ctable <- coef(summary(M.Gross)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Gross,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[1,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ## add covariates M2 | |
| M2.Gross <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.Gross) | |
| ctable <- coef(summary(M2.Gross)) | |
| ## calculate and 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.Gross,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[1,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ## add covariates M3 | |
| M3.Gross <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.Gross) | |
| ctable <- coef(summary(M3.Gross)) | |
| ## calculate and 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.Gross,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[1,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ## repeat without smokers | |
| M2.GrossB <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[!ASQ_df_final$Maternal_Smoking_During_Pregnancy == "yes",], | |
| Hess=TRUE) | |
| summary(M2.GrossB) | |
| ctable <- coef(summary(M2.GrossB)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| Appen_OR2_gross_noSmokers <- ordinal_apa(M2.GrossB,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ## add covariates M3 | |
| M3.GrossB <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[!ASQ_df_final$Maternal_Smoking_During_Pregnancy == "yes",], | |
| Hess=TRUE) | |
| summary(M3.GrossB) | |
| ctable <- coef(summary(M3.GrossB)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| Appen_OR3_gross_noSmokers <- ordinal_apa(M3.GrossB,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ### Interactions #### | |
| ## case vS control | |
| M.Gross_xCaCo <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Gross_xCaCo) | |
| ctable <- coef(summary(M.Gross_xCaCo)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| Appen_OR_gross_CaCo <- ordinal_apa(M.Gross_xCaCo,"caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #Child_Sex | |
| M.Gross_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Gross_xChS) | |
| ctable <- coef(summary(M.Gross_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| Appen_OR_gross_ChS <- ordinal_apa(M.Gross_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #add covariates | |
| M2.Gross_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| Hess=TRUE) | |
| summary(M2.Gross_xChS) | |
| ctable <- coef(summary(M2.Gross_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| Appen_OR2_gross_ChS <- ordinal_apa(M2.Gross_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #add covariates | |
| M3.Gross_xChS <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| Hess=TRUE) | |
| summary(M3.Gross_xChS) | |
| ctable <- coef(summary(M3.Gross_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| Appen_OR3_gross_ChS <- ordinal_apa(M3.Gross_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ## boys only #### | |
| M.Gross_xChS_boys <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| Hess=TRUE) | |
| summary(M.Gross_xChS_boys) | |
| ctable <- coef(summary(M.Gross_xChS_boys)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Gross_xChS_boys,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ##model 2 | |
| M2.Gross_xChS_boys <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| Hess=TRUE) | |
| summary(M2.Gross_xChS_boys) | |
| ctable <- coef(summary(M2.Gross_xChS_boys)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M2.Gross_xChS_boys,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ##Model 3 | |
| M3.Gross_xChS_boys <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + | |
| Weight_Gain_cent + | |
| Maternal_Hypertensive_Disorders_anyVSnone + | |
| Maternal_Diabetes_Disorders_anyVSnone + | |
| Maternal_Smoking_During_Pregnancy + | |
| Gestational_Age_Weeks_cent + | |
| Child_Birth_Weight_cent + | |
| postpartum_Cesd_cent + | |
| postpartum_BAI_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| Hess=TRUE) | |
| summary(M3.Gross_xChS_boys) | |
| ctable <- coef(summary(M3.Gross_xChS_boys)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| OR3_gross_ChS_boys <- ordinal_apa(M3.Gross_xChS_boys,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### girls only #### | |
| M.Gross_xChS_girls <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| Hess=TRUE) | |
| summary(M.Gross_xChS_girls) | |
| ctable <- coef(summary(M.Gross_xChS_girls)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Gross_xChS_girls,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #model 2 | |
| M2.Gross_xChS_girls <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + | |
| Weight_Gain_cent + | |
| Maternal_Hypertensive_Disorders_anyVSnone + | |
| Maternal_Diabetes_Disorders_anyVSnone + | |
| Maternal_Smoking_During_Pregnancy + | |
| Gestational_Age_Weeks_cent + | |
| Child_Birth_Weight_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| Hess=TRUE) | |
| summary(M2.Gross_xChS_girls) | |
| ctable <- coef(summary(M2.Gross_xChS_girls)) | |
| ## calculate and 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.Gross_xChS_girls,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #model3 | |
| M3.Gross_xChS_girls <- polr(factor(Child_ASQ_grossmotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| Hess=TRUE) | |
| summary(M3.Gross_xChS_girls) | |
| ctable <- coef(summary(M3.Gross_xChS_girls)) | |
| ## calculate and 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.Gross_xChS_girls,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Fine Motor Development #### | |
| M.Fine <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Fine) | |
| ctable <- coef(summary(M.Fine)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Fine,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[4,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M2 | |
| M2.Fine <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.Fine) | |
| ctable <- coef(summary(M2.Fine)) | |
| ## calculate and 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.Fine,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[4,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M3 | |
| M3.Fine <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.Fine) | |
| ctable <- coef(summary(M3.Fine)) | |
| ## calculate and 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.Fine,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[4,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Interactions #### | |
| M.Fine_xCaCo <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Fine_xCaCo) | |
| ctable <- coef(summary(M.Fine_xCaCo)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| Appen_OR_fine_CaCo <- ordinal_apa(M.Fine_xCaCo,"caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #Child_Sex | |
| M.Fine_xChS <- polr(factor(Child_ASQ_finemotor_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Fine_xChS) | |
| ctable <- coef(summary(M.Fine_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| Appen_OR_fine_ChS <- ordinal_apa(M.Fine_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ### Communication Development #### | |
| M.Com <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Com) | |
| ctable <- coef(summary(M.Com)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Com,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ## M2 | |
| M2.Com <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.Com) | |
| ctable <- coef(summary(M2.Com)) | |
| ## calculate and 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.Com,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ##M3 | |
| M3.Com <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.Com) | |
| ctable <- coef(summary(M3.Com)) | |
| ## calculate and 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.Com,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Interactions #### | |
| #caseVScontrol | |
| M.Com_xCaCo <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Com_xCaCo) | |
| ctable <- coef(summary(M.Com_xCaCo)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Com_xCaCo,"Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrolcontrol") | |
| Appen_OR_com_CaCo <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #Child Sex | |
| M.Com_xChS <- polr(factor(Child_ASQ_communication_develop_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.Com_xChS) | |
| ctable <- coef(summary(M.Com_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.Com_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_OR_com_ChS <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Personal/Social Development #### | |
| M.per <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.per) | |
| ctable <- coef(summary(M.per)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.per,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[6,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M2 | |
| M2.per <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.per) | |
| ctable <- coef(summary(M2.per)) | |
| ## calculate and 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.per,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[6,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M3 | |
| M3.per <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.per) | |
| ctable <- coef(summary(M3.per)) | |
| ## calculate and 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.per,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[6,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Interactions #### | |
| #caseVScontrol | |
| M.per_xCaCo <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.per_xCaCo) | |
| ctable <- coef(summary(M.per_xCaCo)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.per_xCaCo,"Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrolcontrol") | |
| Appen_OR_per_CaCo <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #Child Sex | |
| M.per_xChS <- polr(factor(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.per_xChS) | |
| ctable <- coef(summary(M.per_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.per_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_OR_per_ChS <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Prob Development #### | |
| M.prob <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.prob) | |
| ctable <- coef(summary(M.prob)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.prob,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[7,2:7] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M2 | |
| M2.prob <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M2.prob) | |
| ctable <- coef(summary(M2.prob)) | |
| ## calculate and 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.prob,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[7,8:13] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #M3 | |
| M3.prob <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M3.prob) | |
| ctable <- coef(summary(M3.prob)) | |
| ## calculate and 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.prob,"Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results[7,14:19] <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ### Interactions #### | |
| #caseVScontrol | |
| M.prob_xCaCo <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.prob_xCaCo) | |
| ctable <- coef(summary(M.prob_xCaCo)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.prob_xCaCo,"Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrolcontrol") | |
| Appen_OR_prob_CaCo <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| #Child Sex | |
| M.prob_xChS <- polr(factor(Child_ASQ_problemsolving_development_infancy_sum_finalagerange) ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| Hess=TRUE) | |
| summary(M.prob_xChS) | |
| ctable <- coef(summary(M.prob_xChS)) | |
| ## calculate and store p values | |
| p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 | |
| ## combined table | |
| ctable <- cbind(ctable, "p value" = p) | |
| res <- ordinal_apa(M.prob_xChS,"Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_OR_prob_ChS <- c(res[c("estimate","SE", "LL", "UL", "t","p")]) | |
| ################################################################################ | |
| ############### Logit ##################### | |
| # ####### Gross motor development score #### | |
| # ### Prenatal Cesd #### | |
| #ASQ and prenatal Cesd | |
| M1_gross <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_gross) | |
| res <- logit_apa(M1_gross, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[1,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M2 | |
| M2_gross.cov <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_gross.cov) | |
| res <- logit_apa(M2_gross.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[1,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd + postnatal covariates | |
| M3_gross.cov <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_gross.cov) | |
| res <- logit_apa(M3_gross.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[1,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| ##ASQ and prenatal Cesd*child sex | |
| M1_gross.INT1 <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M1_gross.INT1) | |
| Appen_LOG_gross_xChS <- logit_apa(M1_gross.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ##M2 | |
| M2_gross.INT1 <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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.exclude", | |
| family = "binomial") | |
| summary(M2_gross.INT1) | |
| Appen_LOG2_gross_xChS <- logit_apa(M2_gross.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| #M3 | |
| M3_gross.INT1 <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| data = ASQ_df_final, | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M3_gross.INT1) | |
| Appen_LOG3_gross_xChS <- logit_apa(M3_gross.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| ## for boys | |
| M1_gross.INT1_boys <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M1_gross.INT1_boys) | |
| res <- logit_apa(M1_gross.INT1_boys, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[2,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ##M2 | |
| M2_gross.INT1_boys <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M2_gross.INT1_boys) | |
| res <- logit_apa(M2_gross.INT1_boys, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[2,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M3 | |
| M3_gross.INT1_boys <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + | |
| Weight_Gain_cent + | |
| Maternal_Hypertensive_Disorders_anyVSnone + | |
| Maternal_Diabetes_Disorders_anyVSnone + | |
| Maternal_Smoking_During_Pregnancy + | |
| Gestational_Age_Weeks_cent + | |
| Child_Birth_Weight_cent + | |
| postpartum_BAI_cent + | |
| postpartum_Cesd_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "boy",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M3_gross.INT1_boys) | |
| res <- logit_apa(M3_gross.INT1_boys, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[2,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ## for girls | |
| M1_gross.INT1_girls <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M1_gross.INT1_girls) | |
| res <- logit_apa(M1_gross.INT1_girls, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[3,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ##M2 | |
| M2_gross.INT1_girls <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_cent + | |
| Weight_Gain_cent + | |
| Maternal_Hypertensive_Disorders_anyVSnone + | |
| Maternal_Diabetes_Disorders_anyVSnone + | |
| Maternal_Smoking_During_Pregnancy + | |
| Gestational_Age_Weeks_cent + | |
| Child_Birth_Weight_cent, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M2_gross.INT1_girls) | |
| res <- logit_apa(M2_gross.INT1_girls, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[3,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M3 | |
| M3_gross.INT1_girls <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final[ASQ_df_final$Child_Sex == "girl",], | |
| na.action = "na.exclude", | |
| family = "binomial") | |
| summary(M3_gross.INT1_girls) | |
| res <- logit_apa(M3_gross.INT1_girls, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[3,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_gross.INT2 <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_gross.INT2) | |
| Appen_LOG_gross_xCaCo <- logit_apa(M1_gross.INT2, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # ### Categorical Cesd #### | |
| #ASQ and prenatal Cesd | |
| M1_gross_cat <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Depressive_Symptom_Severity, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_gross_cat) | |
| #res <- logit_apa(M1_gross_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[10,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M2 | |
| M2_gross.cov_cat <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Depressive_Symptom_Severity + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| family = "binomial") | |
| summary(M2_gross.cov_cat) | |
| #res <- logit_apa(M2_gross.cov_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[10,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd + postnatal covariates | |
| M3_gross.cov_cat <- glm(Child_ASQ_grossmotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Depressive_Symptom_Severity + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_gross.cov_cat) | |
| #res <- logit_apa(M3_gross.cov_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[10,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # ####### Fine motor development score #### | |
| # ### Prenatal Cesd #### | |
| #ASQ and prenatal Cesd | |
| M1_fine <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_fine) | |
| res <- logit_apa(M1_fine, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[4,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M2 | |
| #ASQ and prenatal Cesd + prenatal covariates | |
| M2_fine.cov <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_fine.cov) | |
| res <- logit_apa(M2_fine.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[4,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + postnatal covariates | |
| M3_fine.cov <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_fine.cov) | |
| res <- logit_apa(M3_fine.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[4,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| # #ASQ and prenatal Cesd*child sex | |
| M1_fine.INT1 <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_fine.INT1) | |
| Appen_LOG_fine_xChS <- logit_apa(M1_fine.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_fine.INT2 <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_fine.INT2) | |
| Appen_LOG_fine_xCaCo <- logit_apa(M1_fine.INT2, "caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # ### Categorical Cesd #### | |
| #ASQ and prenatal Cesd | |
| M1_fine_cat <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Depressive_Symptom_Severity, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_fine_cat) | |
| #res <- logit_apa(M1_fine_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[11,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #M2 | |
| M2_fine.cov_cat <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Depressive_Symptom_Severity + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| family = "binomial") | |
| summary(M2_fine.cov_cat) | |
| #res <- logit_apa(M2_fine.cov_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[11,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd + postnatal covariates | |
| M3_fine.cov_cat <- glm(Child_ASQ_finemotor_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Depressive_Symptom_Severity + | |
| caseVScontrol + | |
| Maternal_Age_Years_cent + | |
| Maternal_Education + | |
| Parity + | |
| Maternal_Body_Mass_Index_in_Early_Pregnancy_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_fine.cov_cat) | |
| #res <- logit_apa(M3_fine.cov_cat, "Depressive_Symptom_SeverityClinical (CES-D >= 16)") | |
| #ASQ_subscale_results_ND[11,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # ####### communication development score #### | |
| # ### Prenatal Cesd #### | |
| # #ASQ and prenatal Cesd | |
| M1_com <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_com) | |
| res <- logit_apa(M1_com, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[5,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + prenatal covariates | |
| M2_com.cov <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_com.cov) | |
| res <- logit_apa(M2_com.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[5,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + postnatal covariates | |
| M3_com.cov <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_com.cov) | |
| res <- logit_apa(M3_com.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[5,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| # #ASQ and prenatal Cesd*child sex | |
| M1_com.INT1 <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_com.INT1) | |
| Appen_LOG_com_xChS <- logit_apa(M1_com.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_com.INT2 <- glm(Child_ASQ_communication_develop_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_com.INT2) | |
| Appen_LOG_com_xCaCo <- logit_apa(M1_com.INT2, "caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # ####### personal/social development score #### | |
| # ### Prenatal Cesd #### | |
| # #ASQ and prenatal Cesd | |
| M1_per <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_per) | |
| res <- logit_apa(M1_per, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[6,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + prenatal covariates | |
| M2_per.cov <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_per.cov) | |
| res <- logit_apa(M2_per.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[6,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| # #ASQ and prenatal Cesd + postnatal covariates | |
| M3_per.cov <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_per.cov) | |
| res <- logit_apa(M3_per.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[6,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| # #ASQ and prenatal Cesd*child sex | |
| M1_per.INT1 <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_per.INT1) | |
| Appen_LOG_per_xChS <- logit_apa(M1_per.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_per.INT2 <- glm(Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_per.INT2) | |
| Appen_LOG_per_xCaCo <- logit_apa(M1_per.INT2, "caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # ####### problem solving development score #### | |
| # ### Prenatal Cesd #### | |
| # #ASQ and prenatal Cesd | |
| M1_prob <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_prob) | |
| res <- logit_apa(M1_prob, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[7,2:7] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + prenatal covariates | |
| M2_prob.cov <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M2_prob.cov) | |
| res <- logit_apa(M2_prob.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[7,8:13] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| #ASQ and prenatal Cesd + postnatal covariates | |
| M3_prob.cov <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_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, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M3_prob.cov) | |
| res <- logit_apa(M3_prob.cov, "Cesd_qclosest_to_cortGW_pregMean_cent") | |
| Appen_ASQ_subscale_results_ND[7,14:19] <- c(res[c("estimate","SE", "LL", "UL", "z","p")]) | |
| ### Interactions #### | |
| # #ASQ and prenatal Cesd*child sex | |
| M1_prob.INT1 <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:Child_Sex, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_prob.INT1) | |
| Appen_LOG_prob_xChS <- logit_apa(M1_prob.INT1, "Child_Sexgirl:Cesd_qclosest_to_cortGW_pregMean_cent") | |
| # #ASQ and prenatal Cesd*caseVScontrol | |
| M1_prob.INT2 <- glm(Child_ASQ_problemsolving_development_infancy_sum_finalagerange_norm_dichom ~ | |
| ChildAge_ASQ_months_allchildren_cent + | |
| Child_Sex + | |
| caseVScontrol + | |
| Cesd_qclosest_to_cortGW_pregMean_cent + | |
| Cesd_qclosest_to_cortGW_pregMean_cent:caseVScontrol, | |
| data = ASQ_df_final, | |
| family = "binomial") | |
| summary(M1_prob.INT2) | |
| Appen_LOG_prob_xCaCo <- logit_apa(M1_prob.INT2, "caseVScontrolcontrol:Cesd_qclosest_to_cortGW_pregMean_cent") |