Skip to content
Permalink
master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Master-Thesis-Analyses/Sample_Descriptives.R

Go to file
 
 
Cannot retrieve contributors at this time
742 lines (665 sloc) 35.9 KB
Raw Blame
Open in GitHub Desktop
## Sample Descriptives
#13.07.2021
##ITU: Thesis sample and cortisol characteristics
#22/05/2021
library(dplyr)
library(gtsummary)
library(lubridate)
library(rstatix)
library(irr)
library(gtable)
library(gt)
library(apaTables)
library(flextable)
#setwd("C:/Users/User/Desktop/Internship/RScripts/Cortisol/Master_Thesis")
setwd("C:/Users/alici/Desktop/Git_Folder/ITU_cortisol_analyses/Master_Thesis")
############ Data Preparation ##################################
load("Rdata/ITU_combined_cortisol_dates_times_wide_format.Rdata")
load("Rdata/processed_register_data.Rdata")
medication <- read.delim("Rdata/ITU_psychotrophicmedication05July21_Maternal_CurrentPregnancy.dat")
names(medication)[1] <- "participantID"
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$ITU_1.7y_mother_CESD_sum <- as.numeric(gsub(",", ".", postpartum_followUp$ITU_1.7y_mother_CESD_sum))
postpartum_followUp$ITU_1.7y_mother_CESD_sum_nomis <- as.numeric(gsub(",", ".", postpartum_followUp$ITU_1.7y_mother_CESD_sum_nomis))
postpartum_followUp$ITU_1.7y_mother_BAI_sum_half_missing <- as.numeric(gsub(",", ".", postpartum_followUp$ITU_1.7y_mother_BAI_sum_half_missing))
postpartum_followUp$ITU_1.7y_mother_BAI_sum_no_missing <- as.numeric(gsub(",", ".", postpartum_followUp$ITU_1.7y_mother_BAI_sum_no_missing))
maternal_edu <- read.delim("Rdata/ITU maternal education.dat")
names(maternal_edu)[1] <- "participantID"
names(maternal_edu)[3] <- "Maternal_Education"
final_wide_cort_wR <- left_join(wide_cort,
register_data,
copy=TRUE)
final_wide_cort_wEdu <- left_join(final_wide_cort_wR,
maternal_edu[,-c(2)],
copy=TRUE)
final_wide_cort_wpost <- left_join(final_wide_cort_wEdu,
postpartum_followUp[,c("participantID",
"ITU_1.7y_mother_BAI_sum_no_missing",
"ITU_1.7y_mother_CESD_sum_nomis")],
copy=TRUE)
final_wide_cort <- left_join(final_wide_cort_wpost,
medication[,c("participantID",
"ITUbroadpsychiatricmedication_18_KELA",
"antidepressants_18_KELA")],
copy=TRUE)
##factoring of predictors
final_wide_cort$pregstage <- factor(final_wide_cort$pregstage)
final_wide_cort$participantID <- factor(final_wide_cort$participantID)
final_wide_cort$ELISA_analysis_plate <- factor(final_wide_cort$ELISA_analysis_plate)
final_wide_cort$Maternal_Education <- factor(final_wide_cort$Maternal_Education,
levels = c(1,2,3),
labels = c("Primary/Secondary Education",
"Polytechnic Degree/University of Applied Sciences",
"University Degree"))
final_wide_cort$Current_Diabetes_Disorder <- factor(final_wide_cort$Maternal_Diabetes_Disorders_anyVSnone,
levels = c(-999,0,1),
labels = c("No","No", "Yes"))
final_wide_cort$Current_Hypertensive_Disorder <- factor(final_wide_cort$Maternal_Hypertensive_Disorders_anyVSnone,
levels = c(-999,0,1),
labels = c("No","No", "Yes"))
final_wide_cort$Child_Sex <- factor(final_wide_cort$Child_Sex,
levels = c("boy","girl"),
labels = c("Male", "Female"))
final_wide_cort$Nulliparous <- factor(final_wide_cort$Parity,
levels = c("nulliparous","multiparous"),
labels = c("Yes", "No"))
final_wide_cort$caseVScontrol <- factor(final_wide_cort$caseVScontrol,
levels = c("case","control"),
labels = c("Case", "Control"))
final_wide_cort$ITUbroadpsychiatricmedication_18_KELA <- factor(final_wide_cort$ITUbroadpsychiatricmedication_18_KELA,
levels = c(0,1),
labels = c("no", "yes"))
final_wide_cort$antidepressants_18_KELA <- factor(final_wide_cort$antidepressants_18_KELA,
levels = c(0,1),
labels = c("no", "yes"))
########### add well_being data to it
load("Rdata/processed_wellbeingduringpreg_completevars.Rdata")
#for now only sleep, nausea, anxiety and CESD are included
cols <- c("1", "2",grep("gestage", names(q_data_complete)),
grep("GWdiff_cort_qclosest", names(q_data_complete)),
grep("Cesd", names(q_data_complete)),
#grep("puqe", names(q_data_complete)),
grep("PSQI", names(q_data_complete)),
# grep("ESS", names(q_data_complete)),
grep("BAI",names(q_data_complete)))
q_data_sub <- q_data_complete[,c(as.numeric(cols))]
q_data_sub <- subset(q_data_sub, abs(GWdiff_cort_qclosest_to_cortGW) < 2)
# length(unique(q_data_sub$participantID[!is.na(q_data_sub$Cesd_qclosest_to_cortGW_pregMean)]))
# length(unique(q_data_sub$participantID[!is.na(q_data_sub$Cesd_qclosest_to_cortGW) & q_data_sub$pregstage == "I"]))
# length(unique(q_data_sub$participantID[!is.na(q_data_sub$Cesd_qclosest_to_cortGW) & q_data_sub$pregstage == "II"]))
# length(unique(q_data_sub$participantID[!is.na(q_data_sub$Cesd_qclosest_to_cortGW) & q_data_sub$pregstage == "III"]))
#combine with cortisol
final_wide_cort <- left_join(final_wide_cort,
q_data_sub,
by = c("participantID", "pregstage", "gestage_weeks"),
copy=TRUE)
final_wide_cort <- final_wide_cort %>%
group_by(participantID) %>%
mutate(Cesd_qclosest_to_cortGW_pregMean = mean(Cesd_qclosest_to_cortGW_pregMean, na.rm =T))
final_wide_cort <- final_wide_cort %>%
group_by(participantID) %>%
mutate(PSQI_qclosest_to_cortGW_pregMean = mean(PSQI_qclosest_to_cortGW_pregMean, na.rm =T))
final_wide_cort <- final_wide_cort %>%
group_by(participantID) %>%
mutate(BAI_qclosest_to_cortGW_pregMean = mean(BAI_qclosest_to_cortGW_pregMean, na.rm =T))
final_wide_cort <- mutate_all(final_wide_cort,funs(replace(., is.nan(.), NA)))
#average gestage_weeks of cortisol assessment
final_wide_cort <- final_wide_cort %>%
group_by(participantID) %>%
mutate(gestage_weeks_pregMean = mean(gestage_weeks, na.rm =T))
### Exclusion Criteria ####
#exclude violattions against cortisol collection protocol
comments_to_be_excluded <- c(88,6,12,3,15,9999,7,16,45,5,34,17,57,67,99)
final_wide_cort <- subset(final_wide_cort, !(final_wide_cort$notes %in% comments_to_be_excluded))
final_wide_cort <- subset(final_wide_cort, !(final_wide_cort$Maternal_Corticosteroid_Treatment_during_Pregnancy == "yes"))
### Depressive Symptom Severity ####
final_wide_cort$Depressive_Symptom_Severity <- NA
for(i in 1:nrow(final_wide_cort)){
m <- final_wide_cort$Cesd_qclosest_to_cortGW_pregMean[[i]]
if(!is.na(m)){
if(m<16){
final_wide_cort$Depressive_Symptom_Severity[[i]] <- "Below_Clinical (CES-D < 16)"
}
if(m>=16){
final_wide_cort$Depressive_Symptom_Severity[[i]] <- "Clinical (CES-D >= 16)"
}
}
}
final_wide_cort$Depressive_Symptom_Severity <- factor(final_wide_cort$Depressive_Symptom_Severity)
rm(list= ls()[!(ls() %in% c("final_wide_cort"))])
# length(unique(final_wide_cort$participantID))
# length(unique(final_wide_cort$participantID[final_wide_cort$pregstage == "I"]))
# length(unique(final_wide_cort$participantID[final_wide_cort$pregstage == "II"]))
# length(unique(final_wide_cort$participantID[final_wide_cort$pregstage == "III"]))
#final_wide_cort <- final_wide_cort[abs(final_wide_cort$GWdiff_cort_qclosest_to_cortGW) < 2,]
# length(unique(final_wide_cort$participantID[!is.na(final_wide_cort$Cesd_qclosest_to_cortGW_pregMean)]))
# length(unique(final_wide_cort$participantID[!is.na(final_wide_cort$Cesd_qclosest_to_cortGW) & final_wide_cort$pregstage == "I"]))
# length(unique(final_wide_cort$participantID[!is.na(final_wide_cort$Cesd_qclosest_to_cortGW) & final_wide_cort$pregstage == "II"]))
# length(unique(final_wide_cort$participantID[!is.na(final_wide_cort$Cesd_qclosest_to_cortGW) & final_wide_cort$pregstage == "III"]))
### Pregstage in wide format ####
Wide_Pregstage <- final_wide_cort[,c("participantID",
"pregstage",
"gestage_weeks")] %>%
tidyr::pivot_wider(
id_cols = c(participantID),
names_from = c(pregstage), # Can accommodate more variables, if needed.
values_from = c(gestage_weeks)
)
final_wide_cort <- left_join(final_wide_cort,
Wide_Pregstage,
copy=T)
### Tab: Demographics ####
characteristics <- final_wide_cort[,c("participantID",
"caseVScontrol",
"I",
"II",
"III",
"Nulliparous",
"Maternal_Age_Years",
"Maternal_Education",
"Mother_Cohabiting",
"gestage_weeks_pregMean",
"Child_Sex",
"Gestational_Age_Weeks",
"Maternal_Body_Mass_Index_in_Early_Pregnancy",
"Weight_Gain",
"Current_Hypertensive_Disorder",
"Current_Diabetes_Disorder",
"Maternal_Smoking_During_Pregnancy",
"Cesd_qclosest_to_cortGW_pregMean",
"Depressive_Symptom_Severity",
"BAI_qclosest_to_cortGW_pregMean",
"PSQI_qclosest_to_cortGW_pregMean",
"ITUbroadpsychiatricmedication_18_KELA",
"antidepressants_18_KELA",
"ITU_1.7y_mother_CESD_sum_nomis",
"ITU_1.7y_mother_BAI_sum_no_missing")]
characteristics_no_dub <- as.data.frame(characteristics[!duplicated(characteristics),]) #N=687
any_med <- length(unique(characteristics_no_dub$participantID[characteristics_no_dub$ITUbroadpsychiatricmedication_18_KELA == "yes"]))
anti_depress <- length(unique(characteristics_no_dub$participantID[characteristics_no_dub$antidepressants_18_KELA == "yes"]))
percent_depress <- format(round(anti_depress/any_med*100,2))
demo <- characteristics_no_dub %>% select(
I,II,III,
caseVScontrol,
Maternal_Age_Years,
Maternal_Education,
Mother_Cohabiting,
Nulliparous,
Child_Sex,
Gestational_Age_Weeks,
Maternal_Body_Mass_Index_in_Early_Pregnancy,
Weight_Gain,
Current_Hypertensive_Disorder,
Current_Diabetes_Disorder,
Maternal_Smoking_During_Pregnancy,
ITUbroadpsychiatricmedication_18_KELA,
Cesd_qclosest_to_cortGW_pregMean,
Depressive_Symptom_Severity,
BAI_qclosest_to_cortGW_pregMean,
PSQI_qclosest_to_cortGW_pregMean,
ITU_1.7y_mother_CESD_sum_nomis,
ITU_1.7y_mother_BAI_sum_no_missing)
# Sample_Descriptives1 <-
# tbl_summary(data = demo,
# statistic = list(
# all_continuous() ~ "{mean} ({sd})",
# all_categorical() ~ "{n} ({p}%)"),
# missing = "no",
# digits = list(all_continuous() ~ c(2, 2, 2)),
# label = list(Maternal_Age_Years ~ "Maternal Age (Yrs)",
# Maternal_Education ~ "Highest Level of Maternal Education",
# Mother_Cohabiting ~ "Maternal Cohabitation",
# Maternal_Body_Mass_Index_in_Early_Pregnancy ~ "Maternal BMI in Early Pregnancy",
# Child_Sex ~ "Fetal Sex",
# Maternal_Smoking_During_Pregnancy ~ "Maternal Smoking throughout Pregnancy",
# Weight_Gain ~ "Weight Gain across Pregnancy (kg)",
# Current_Hypertensive_Disorder ~ "Any Hypertensive Disorders during Pregnancy",
# Current_Diabetes_Disorder ~ "Any Diabetes Disorders during Pregnancy",
# Cesd_qclosest_to_cortGW_pregMean ~ "Peripartum Depressive Symptoms",
# BAI_qclosest_to_cortGW_pregMean ~ "Peripartum Anxiety",
# PSQI_qclosest_to_cortGW_pregMean ~ "Peripartum Sleep Problems",
# ITUbroadpsychiatricmedication_18_KELA ~ "Any Prescribed Psychotropic Medication",
# ITU_1.7y_mother_CESD_sum_nomis ~ "Postpartum Depressive Symptoms",
# ITU_1.7y_mother_BAI_sum_no_missing ~ "Postpartum Anxiety",
# I ~ "Gestational Week at T1",
# II ~ "Gestational Week at T2",
# III ~ "Gestational Week at T3"))
Sample_Descriptives2 <-
tbl_summary(data = demo,
by = Depressive_Symptom_Severity,
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
missing = "no",
digits = list(all_continuous() ~ c(2, 2, 2)),
label = list(Maternal_Age_Years ~ "Maternal Age (Yrs)",
Maternal_Education ~ "Highest Level of Maternal Education",
Mother_Cohabiting ~ "Maternal Cohabitation",
Maternal_Body_Mass_Index_in_Early_Pregnancy ~ "Maternal BMI in Early Pregnancy",
Child_Sex ~ "Fetal Sex",
Maternal_Smoking_During_Pregnancy ~ "Maternal Smoking throughout Pregnancy",
Weight_Gain ~ "Weight Gain across Pregnancy (kg)",
Current_Hypertensive_Disorder ~ "Any Hypertensive Disorders during Pregnancy",
Current_Diabetes_Disorder ~ "Any Diabetes Disorders during Pregnancy",
Cesd_qclosest_to_cortGW_pregMean ~ "Peripartum Depressive Symptoms",
BAI_qclosest_to_cortGW_pregMean ~ "Peripartum Anxiety",
PSQI_qclosest_to_cortGW_pregMean ~ "Peripartum Sleep Problems",
ITUbroadpsychiatricmedication_18_KELA ~ "Any Prescribed Psychotropic Medication",
ITU_1.7y_mother_CESD_sum_nomis ~ "Postpartum Depressive Symptoms",
ITU_1.7y_mother_BAI_sum_no_missing ~ "Postpartum Anxiety",
I ~ "Gestational Week at T1",
II ~ "Gestational Week at T2",
III ~ "Gestational Week at T3")) %>% add_overall()
#add p-value
Sample_Descriptives2 <- add_p(Sample_Descriptives2,
test = list(all_continuous() ~ "kruskal.test", all_categorical() ~ "chisq.test"),
pvalue_fun = purrr::partial(style_pvalue, digits = 3))
# Sample_Descriptives <- tbl_merge(
# tbls = list(Sample_Descriptives1, Sample_Descriptives2),
# tab_spanner = c("**Total**", "**Depressive Symptom Severity**")
# )
Sample_Descriptives.Tab <- as_flex_table(Sample_Descriptives2) %>%
font(fontname = "Times New ROman") %>%
fontsize(size = 12) %>%
align(i=1,j=1,align="left") %>%
set_table_properties(layout = "autofit") %>%
footnote(
i=26,
j=1,
value = as_paragraph(c(paste("Of which", percent_depress, "% were prescribed antidepressant medication during pregnancy"))),
ref_symbols = c("3"))
### Tab: Questionnaire summary statistics across Pregnancy ###############################
final_wide_cort <- as.data.frame(final_wide_cort)
Qs_df <- final_wide_cort %>%
select(
participantID,
pregstage,
Cesd_qclosest_to_cortGW,
BAI_qclosest_to_cortGW,
PSQI_qclosest_to_cortGW
)
Qs_df <- Qs_df %>%
tidyr::pivot_wider(
id_cols = c(participantID,
pregstage),
names_from = c(pregstage), # Can accommodate more variables, if needed.
values_from = c(3:5)
)
Qs_by_pregstage_tab <- setNames(data.frame(matrix(ncol = 5, nrow = 12)),
c("Variable",
"M",
"min",
"max",
"SD"))
Qs_by_pregstage_tab[,1] <- c("Early Pregnancy",
"CES-D",
"BAI",
"PSQI",
"Mid Pregnancy",
"CES-D",
"BAI",
"PSQI",
"Late Pregnancy",
"CES-D",
"BAI",
"PSQI")
#early preg
Qs_by_pregstage_tab[2,2] <- c(mean(Qs_df$Cesd_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[2,3] <- c(min(Qs_df$Cesd_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[2,4] <- c(max(Qs_df$Cesd_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[2,5] <- c(sd(Qs_df$Cesd_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[3,2] <- c(mean(Qs_df$BAI_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[3,3] <- c(min(Qs_df$BAI_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[3,4] <- c(max(Qs_df$BAI_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[3,5] <- c(sd(Qs_df$BAI_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[4,2] <- c(mean(Qs_df$PSQI_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[4,3] <- c(min(Qs_df$PSQI_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[4,4] <- c(max(Qs_df$PSQI_qclosest_to_cortGW_I, na.rm = T))
Qs_by_pregstage_tab[4,5] <- c(sd(Qs_df$PSQI_qclosest_to_cortGW_I, na.rm = T))
#mid preg
Qs_by_pregstage_tab[6,2] <- c(mean(Qs_df$Cesd_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[6,3] <- c(min(Qs_df$Cesd_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[6,4] <- c(max(Qs_df$Cesd_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[6,5] <- c(sd(Qs_df$Cesd_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[7,2] <- c(mean(Qs_df$BAI_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[7,3] <- c(min(Qs_df$BAI_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[7,4] <- c(max(Qs_df$BAI_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[7,5] <- c(sd(Qs_df$BAI_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[8,2] <- c(mean(Qs_df$PSQI_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[8,3] <- c(min(Qs_df$PSQI_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[8,4] <- c(max(Qs_df$PSQI_qclosest_to_cortGW_II, na.rm = T))
Qs_by_pregstage_tab[8,5] <- c(sd(Qs_df$PSQI_qclosest_to_cortGW_II, na.rm = T))
#late preg
Qs_by_pregstage_tab[10,2] <- c(mean(Qs_df$Cesd_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[10,3] <- c(min(Qs_df$Cesd_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[10,4] <- c(max(Qs_df$Cesd_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[10,5] <- c(sd(Qs_df$Cesd_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[11,2] <- c(mean(Qs_df$BAI_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[11,3] <- c(min(Qs_df$BAI_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[11,4] <- c(max(Qs_df$BAI_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[11,5] <- c(sd(Qs_df$BAI_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[12,2] <- c(mean(Qs_df$PSQI_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[12,3] <- c(min(Qs_df$PSQI_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[12,4] <- c(max(Qs_df$PSQI_qclosest_to_cortGW_III, na.rm = T))
Qs_by_pregstage_tab[12,5] <- c(sd(Qs_df$PSQI_qclosest_to_cortGW_III, na.rm = T))
### Intraclass Coefficients and correlations #####
Qs_pregMean <- final_wide_cort[,
c("Cesd_qclosest_to_cortGW_pregMean",
"BAI_qclosest_to_cortGW_pregMean",
"PSQI_qclosest_to_cortGW_pregMean",
"ITU_1.7y_mother_BAI_sum_no_missing",
"ITU_1.7y_mother_CESD_sum_nomis")]
Thesis_Q_tabl.pregMean <- apa.cor.table(Qs_pregMean, filename = "Corr_Qs_means.doc", table.number = 4)
#Questionnaire scores at T1
Qs_T1 <- final_wide_cort[final_wide_cort$pregstage == "I",
c("Cesd_qclosest_to_cortGW",
"BAI_qclosest_to_cortGW",
"PSQI_qclosest_to_cortGW")]
Thesis_Q_tabl.T1 <- apa.cor.table(Qs_T1, filename = "Corr_Qs_T1.doc",table.number = 4 )
#Questionnaire scores at T2
Qs_T2 <- final_wide_cort[final_wide_cort$pregstage == "II",
c("Cesd_qclosest_to_cortGW",
"BAI_qclosest_to_cortGW",
"PSQI_qclosest_to_cortGW")]
Thesis_Q_tabl.T2 <-apa.cor.table(Qs_T2,filename = "Corr_Qs_T2.doc",table.number = 4)
#Questionnaire scores at T3
Qs_T3 <- final_wide_cort[final_wide_cort$pregstage == "III",
c("Cesd_qclosest_to_cortGW",
"BAI_qclosest_to_cortGW",
"PSQI_qclosest_to_cortGW")]
Thesis_Q_tabl.T3 <-apa.cor.table(Qs_T3,filename = "Corr_Qs_T3.doc",table.number = 4)
### Questionnaires correlations with themselves #####
#CESD with itself across preg
fully_wide_CESD <- final_wide_cort[,c("participantID",
"pregstage",
"Cesd_qclosest_to_cortGW")] %>%
tidyr::pivot_wider(
id_cols = c(participantID),
names_from = c(pregstage), # Can accommodate more variables, if needed.
values_from = c(3)
)
# plot(factor(final_wide_cort$pregstage), final_wide_cort$Cesd_qclosest_to_cortGW,
# xlab = "Pregstage",
# ylab = "Depressive Symptoms")
#compute correlations across preg
cors_Cesd <- fully_wide_CESD[,c("I", "II", "III")]
corr_CESD <- apa.cor.table(cors_Cesd)
## ICC
ICC_CESD <- icc(
fully_wide_CESD[,c("I", "II", "II")], model = "oneway",
type = "agreement", unit = "single"
)
##within subject CES-D flucutations
#load("Rdata/nr_cesd.Rdata")
# nr_Ids <- unique(ASQ_df_final$participantID[ASQ_df_final$nr_cesds > 1])
# res_mean_CESD_fluctuation <- summary(final_wide_cort$Cesd_qclosest_to_cortGW_pregMeanDeviat[final_wide_cort$participantID %in% nr_Ids])
##### BAI
fully_wide_BAI <- final_wide_cort[,c("participantID",
"pregstage",
"BAI_qclosest_to_cortGW")] %>%
tidyr::pivot_wider(
id_cols = c(participantID),
names_from = c(pregstage), # Can accommodate more variables, if needed.
values_from = c(3)
)
#compute correlations across preg
cors_BAI <- fully_wide_BAI[,c("I", "II", "III")]
corr_BAI <- apa.cor.table(cors_BAI)
## ICC
ICC_BAI <- icc(
fully_wide_BAI[,c("I", "II", "III")], model = "oneway",
type = "agreement", unit = "single"
)
# plot(factor(final_wide_cort$pregstage), final_wide_cort$BAI_qclosest_to_cortGW,
# xlab = "Pregstage",
# ylab = "Anxiety")
##### PSQI
fully_wide_PSQI <- final_wide_cort[,c("participantID",
"pregstage",
"PSQI_qclosest_to_cortGW")] %>%
tidyr::pivot_wider(
id_cols = c(participantID),
names_from = c(pregstage), # Can accommodate more variables, if needed.
values_from = c(3)
)
#compute correlations across preg
cors_PSQI <- fully_wide_PSQI[,c("I", "II", "III")]
corr_PSQI <- apa.cor.table(cors_PSQI)
## ICC
ICC_PSQI <- icc(
fully_wide_PSQI[,c("I", "II", "III")], model = "oneway",
type = "agreement", unit = "single"
)
############ Tab: Follow-up comparison of participants assessed once vs multiple times ################
at_least_two_assessments <- c()
once <- c()
final_wide_cort <- as.data.frame(final_wide_cort)
IDs <- unique(final_wide_cort$participantID)
for(i in 1:length(IDs)){
ID <- IDs[[i]]
#print(ID)
trims <- unique(final_wide_cort[final_wide_cort$participantID == ID, c("pregstage")])
#print(nrow(trims))
if(length(trims) >= 2){
at_least_two_assessments <- append(at_least_two_assessments, ID)
}
if(length(trims) == 1){
once <- append(once, ID)
}
}
## add column identifier
final_wide_cort$Nr_assessments <- NA
for(i in 1:nrow(final_wide_cort)){
ID <- final_wide_cort$participantID[[i]]
if(ID %in% once){
final_wide_cort$Nr_assessments[[i]] <- "Once"
}
if(ID %in% at_least_two_assessments){
final_wide_cort$Nr_assessments[[i]] <- "Multiple"
}
}
characteristics <- final_wide_cort[,c("participantID",
"caseVScontrol",
"I",
"II",
"III",
"Nulliparous",
"Maternal_Age_Years",
"Maternal_Education",
"Mother_Cohabiting",
"gestage_weeks_pregMean",
"Child_Sex",
"Gestational_Age_Weeks",
"Maternal_Body_Mass_Index_in_Early_Pregnancy",
"Weight_Gain",
"Current_Hypertensive_Disorder",
"Current_Diabetes_Disorder",
"Maternal_Smoking_During_Pregnancy",
"Cesd_qclosest_to_cortGW_pregMean",
"Depressive_Symptom_Severity",
"BAI_qclosest_to_cortGW_pregMean",
"PSQI_qclosest_to_cortGW_pregMean",
"ITUbroadpsychiatricmedication_18_KELA",
"antidepressants_18_KELA",
"ITU_1.7y_mother_CESD_sum_nomis",
"ITU_1.7y_mother_BAI_sum_no_missing",
"Nr_assessments")]
characteristics_no_dub <- as.data.frame(characteristics[!duplicated(characteristics),]) #N=687
demo <- characteristics_no_dub %>% select(
I,II,III,
caseVScontrol,
Maternal_Age_Years,
Maternal_Education,
Mother_Cohabiting,
Nulliparous,
Child_Sex,
Gestational_Age_Weeks,
Maternal_Body_Mass_Index_in_Early_Pregnancy,
Weight_Gain,
Current_Hypertensive_Disorder,
Current_Diabetes_Disorder,
Maternal_Smoking_During_Pregnancy,
ITUbroadpsychiatricmedication_18_KELA,
Cesd_qclosest_to_cortGW_pregMean,
Depressive_Symptom_Severity,
BAI_qclosest_to_cortGW_pregMean,
PSQI_qclosest_to_cortGW_pregMean,
ITU_1.7y_mother_CESD_sum_nomis,
ITU_1.7y_mother_BAI_sum_no_missing,
Nr_assessments)
Sample_Descriptives2 <-
tbl_summary(data = demo,
by = Nr_assessments,
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
missing = "no",
digits = list(all_continuous() ~ c(2, 2, 2)),
label = list(Maternal_Age_Years ~ "Maternal Age (Yrs)",
Maternal_Education ~ "Highest Level of Maternal Education",
Mother_Cohabiting ~ "Maternal Cohabitation",
Maternal_Body_Mass_Index_in_Early_Pregnancy ~ "Maternal BMI in Early Pregnancy",
Child_Sex ~ "Fetal Sex",
Maternal_Smoking_During_Pregnancy ~ "Maternal Smoking throughout Pregnancy",
Weight_Gain ~ "Weight Gain across Pregnancy (kg)",
Current_Hypertensive_Disorder ~ "Any Hypertensive Disorders during Pregnancy",
Current_Diabetes_Disorder ~ "Any Diabetes Disorders during Pregnancy",
Cesd_qclosest_to_cortGW_pregMean ~ "Peripartum Depressive Symptoms",
BAI_qclosest_to_cortGW_pregMean ~ "Peripartum Anxiety",
PSQI_qclosest_to_cortGW_pregMean ~ "Peripartum Sleep Problems",
ITUbroadpsychiatricmedication_18_KELA ~ "Any Prescribed Psychotropic Medication",
ITU_1.7y_mother_CESD_sum_nomis ~ "Postpartum Depressive Symptoms",
ITU_1.7y_mother_BAI_sum_no_missing ~ "Postpartum Anxiety",
I ~ "Gestational Week at T1",
II ~ "Gestational Week at T2",
III ~ "Gestational Week at T3")) %>% add_overall()
#add p-value
Sample_Descriptives2 <- add_p(Sample_Descriptives2,
test = list(all_continuous() ~ "kruskal.test", all_categorical() ~ "chisq.test"),
pvalue_fun = purrr::partial(style_pvalue, digits = 3))
Sample_Descriptives.Sen1 <- as_flex_table(Sample_Descriptives2) %>%
font(fontname = "Times New ROman") %>%
fontsize(size = 9) %>%
align(i=1,j=1,align="left") %>%
set_table_properties(layout = "autofit")
############ Tab: Follow-up comparison of participants by medication ########
characteristics <- final_wide_cort[,c("participantID",
"caseVScontrol",
"I",
"II",
"III",
"Nulliparous",
"Maternal_Age_Years",
"Maternal_Education",
"Mother_Cohabiting",
"gestage_weeks_pregMean",
"Child_Sex",
"Gestational_Age_Weeks",
"Maternal_Body_Mass_Index_in_Early_Pregnancy",
"Weight_Gain",
"Current_Hypertensive_Disorder",
"Current_Diabetes_Disorder",
"Maternal_Smoking_During_Pregnancy",
"Cesd_qclosest_to_cortGW_pregMean",
"Depressive_Symptom_Severity",
"BAI_qclosest_to_cortGW_pregMean",
"PSQI_qclosest_to_cortGW_pregMean",
"ITUbroadpsychiatricmedication_18_KELA",
"antidepressants_18_KELA",
"ITU_1.7y_mother_CESD_sum_nomis",
"ITU_1.7y_mother_BAI_sum_no_missing")]
characteristics_no_dub <- as.data.frame(characteristics[!duplicated(characteristics),]) #N=687
demo <- characteristics_no_dub %>% select(
I,II,III,
caseVScontrol,
Maternal_Age_Years,
Maternal_Education,
Mother_Cohabiting,
Nulliparous,
Child_Sex,
Gestational_Age_Weeks,
Maternal_Body_Mass_Index_in_Early_Pregnancy,
Weight_Gain,
Current_Hypertensive_Disorder,
Current_Diabetes_Disorder,
Maternal_Smoking_During_Pregnancy,
ITUbroadpsychiatricmedication_18_KELA,
Cesd_qclosest_to_cortGW_pregMean,
Depressive_Symptom_Severity,
BAI_qclosest_to_cortGW_pregMean,
PSQI_qclosest_to_cortGW_pregMean,
ITU_1.7y_mother_CESD_sum_nomis,
ITU_1.7y_mother_BAI_sum_no_missing)
Sample_Descriptives2 <-
tbl_summary(data = demo,
by = ITUbroadpsychiatricmedication_18_KELA,
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
missing = "no",
digits = list(all_continuous() ~ c(2, 2, 2)),
label = list(Maternal_Age_Years ~ "Maternal Age (Yrs)",
Maternal_Education ~ "Highest Level of Maternal Education",
Mother_Cohabiting ~ "Maternal Cohabitation",
Maternal_Body_Mass_Index_in_Early_Pregnancy ~ "Maternal BMI in Early Pregnancy",
Child_Sex ~ "Fetal Sex",
Maternal_Smoking_During_Pregnancy ~ "Maternal Smoking throughout Pregnancy",
Weight_Gain ~ "Weight Gain across Pregnancy (kg)",
Current_Hypertensive_Disorder ~ "Any Hypertensive Disorders during Pregnancy",
Current_Diabetes_Disorder ~ "Any Diabetes Disorders during Pregnancy",
Cesd_qclosest_to_cortGW_pregMean ~ "Peripartum Depressive Symptoms",
BAI_qclosest_to_cortGW_pregMean ~ "Peripartum Anxiety",
PSQI_qclosest_to_cortGW_pregMean ~ "Peripartum Sleep Problems",
ITU_1.7y_mother_CESD_sum_nomis ~ "Postpartum Depressive Symptoms",
ITU_1.7y_mother_BAI_sum_no_missing ~ "Postpartum Anxiety",
I ~ "Gestational Week at T1",
II ~ "Gestational Week at T2",
III ~ "Gestational Week at T3")) %>% add_overall()
#add p-value
Sample_Descriptives2 <- add_p(Sample_Descriptives2,
test = list(all_continuous() ~ "kruskal.test", all_categorical() ~ "chisq.test"),
pvalue_fun = purrr::partial(style_pvalue, digits = 3))
Sample_Descriptives.Sen2 <- as_flex_table(Sample_Descriptives2) %>%
font(fontname = "Times New ROman") %>%
fontsize(size = 9) %>%
align(i=1,j=1,align="left") %>%
set_table_properties(layout = "autofit")
############ Tab: Follow-up Qs by season #########
############ Tab: Follow-up comparison of participants by medication ########
characteristics <- final_wide_cort[,c("gestage_weeks",
"Cesd_qclosest_to_cortGW_pregMean",
"BAI_qclosest_to_cortGW_pregMean",
"PSQI_qclosest_to_cortGW_pregMean",
"season")]
demo <- characteristics %>% select(
gestage_weeks,
Cesd_qclosest_to_cortGW_pregMean,
BAI_qclosest_to_cortGW_pregMean,
PSQI_qclosest_to_cortGW_pregMean,
season)
Sample_Descriptives2 <-
tbl_summary(data = demo,
by = season,
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
missing = "no",
digits = list(all_continuous() ~ c(2, 2, 2)),
label = list(gestage_weeks ~ "Average Gestational Week",
Cesd_qclosest_to_cortGW_pregMean ~ "Antepartum Depressive Symptoms",
BAI_qclosest_to_cortGW_pregMean ~ "Antepartum Anxiety",
PSQI_qclosest_to_cortGW_pregMean ~ "Antepartum Sleep Problems")) %>% add_overall()
#add p-value
Sample_Descriptives2 <- add_p(Sample_Descriptives2,
test = list(all_continuous() ~ "kruskal.test", all_categorical() ~ "chisq.test"),
pvalue_fun = purrr::partial(style_pvalue, digits = 3))
Sample_Descriptives.Qs_season <- as_flex_table(Sample_Descriptives2) %>%
font(fontname = "Times New ROman") %>%
fontsize(size = 9) %>%
align(i=1,j=1,align="left") %>%
set_table_properties(layout = "autofit")