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analysis.Rmd
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analysis.Rmd
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---
title: "anc-pregnancy-loss-lbw"
author: "Population Health"
output:
Grmd::docx_document
---
<style type="text/css">
body {
max-width: 100em !important;
}
</style>
```{r setup, include=FALSE}
options(java.parameters = "-Xmx15g")
knitr::opts_chunk$set(warning=FALSE,
message=FALSE,
echo=FALSE,
#dpi=96,
# fig.width=7,# fig.height=4, # Default figure widths
# dev="png", #dev.args=list(type="cairo"), # The png device
# Change to dev="postscript" if you want the EPS-files
# for submitting. Also remove the dev.args() as the postscript
# doesn't accept the type="cairo" argument.
error=FALSE)
# Evaluate the figure caption after the plot
#knitr::opts_knit$set(eval.after='fig.cap')
# Use the table counter that the htmlTable() provides
options(table_counter = TRUE)
# Use the figCapNo() with roman letters
#options(fig_caption_no_roman = TRUE)
#options(kableExtra.latex.load_packages = F)
# Then install the Grmd-package by running below code:
#devtools::install_github("gforge/Grmd")
#devtools::install_github("kassambara/easyGgplot2")
library(easyGgplot2)
# function to install missing packages
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE, repos='http://cran.rstudio.com/')
sapply(pkg, require, character.only = TRUE)
}
#install.packages('package_name', dependencies=TRUE, repos='http://cran.rstudio.com/')
packages =c( "dplyr", "readxl","Hmisc","Gmisc", "magrittr", "flextable", "MASS", "tidyverse", "caret", "knitr", "kableExtra","xtable", "stargazer", "ggpubr")
ipak(packages)
select = dplyr::select; summarize = dplyr::summarize; rename = dplyr::rename; mutate = dplyr::mutate;
source("unbalanced_functions.R")
```
## Utils
```{r warning=FALSE}
describeMissing= function (x, html = TRUE, number_first = TRUE, percentage_sign = TRUE,
language = "en", useNA.digits = 1, ...) {
if (!any(is.na(x)))
return(invisible())
df_arg_list <- list(x = is.na(x), html = html, number_first = number_first,
percentage_sign = percentage_sign, language = language,
digits = useNA.digits)
dot_args <- list(...)
for (n in names(dot_args)) {
if (!n %in% names(df_arg_list)) {
df_arg_list[[n]] <- dot_args[[n]]
}
}
missing <- fastDoCall(describeFactors, df_arg_list)
rownames(missing)<-c("FALSE","Missing")
return(missing["Missing", ])
}
desc_both <- function(x, ...) {
result <- c(
describeMean(x, useNA="no"),
describeMedian(x, useNA="no"),
describeMissing(x)
)
return(result)
}
desc_mean <- function(x, ...) {
result <- c(
describeMean(x, useNA="no"),
describeMissing(x)
)
return(result)
}
desc_median <- function(x, ...) {
result <- c(
describeMedian(x, useNA="no"),
describeMissing(x)
)
return(result)
}
MainX<<-NULL
# Creating a wrapper for getting descriptive statistics
getTable1Stats <- function(x, y, cont_fx=desc_both, data=dataset, digits = 1,statistics = T,na.rm=na.rm.var){
MainX <<- append(MainX, c(x))
data=data%>%drop_na(all_of(y))
if(na.rm){
data=data%>%drop_na(all_of(x))
}
getDescriptionStatsBy(x = data[[x]],
by = data[[y]],
digits = digits,
statistics = statistics,
continuous_fn = cont_fx,
hrzl_prop = T,
total_col_show_perc = T,
header_count = TRUE)
}
relevelBy <- function(varList, data){
for (factorName in names(varList)) {
for(var in varList[[factorName]]){
# print(var)
data[[var]] =relevel(factor(data[[var]]),factorName)
}
}
return(data)
}
getMultipleTable1Stats <- function(x, y,y2,y3, cont_fx=desc_both, data=dataset, digits = 1,statistics = T,hrzl_prop = F,na.rm=na.rm.var){
# data=data%>%drop_na(all_of(y))
if(na.rm){
data=data%>%drop_na(all_of(x))
}
yy1= getDescriptionStatsBy(x = data[[x]],
by = data[[y]],
digits = digits,
statistics = statistics,
continuous_fn = cont_fx,
hrzl_prop = hrzl_prop,
total_col_show_perc = T,
header_count = TRUE)
yy2= getDescriptionStatsBy(x = data[[x]],
by = data[[y2]],
digits = digits,
statistics = statistics,
continuous_fn = cont_fx,
hrzl_prop = hrzl_prop,
total_col_show_perc = T,
header_count = TRUE)
yy3= getDescriptionStatsBy(x = data[[x]],
by = data[[y3]],
digits = digits,
statistics = statistics,
continuous_fn = cont_fx,
hrzl_prop = hrzl_prop,
total_col_show_perc = T,
header_count = TRUE)
result <- tryCatch({
return(cbind(yy1,yy2,yy3))
}, warning = function(war) {
# warning handler picks up where error was generated
print(paste(x,"ERROR"))
print(paste("MY_WARNING: ",war))
}, error = function(err) {
# error handler picks up where error was generated
print(paste(x,"ERROR"))
print(paste("MY_ERROR: ",err))
#return(f)
}, finally = {
# print(paste(x,"Done"))
}) # END tryCatch
}
stargazer2 <- function(model, odd.ratio = F, ...) {
if(!("list" %in% class(model))) model <- list(model)
if (odd.ratio) {
#exponentiate <- function(x) exp(x)
coefOR2 <- lapply(model, function(x) exp(coef(x)))
CIOR2 <- lapply(model, function(x) exp(confint(x)))
seOR2 <- lapply(model, function(x) exp(coef(x)) * summary(x)$coef[, 2])
p2 <- lapply(model, function(x) summary(x)$coefficients[, 4])
stargazer(model, coef = coefOR2, se = seOR2, p = p2, ci.custom =CIOR2, ...)
} else {
stargazer(model, ...)
}
}
generateInterpretation= function(outcome,topn,model.df){
model.df%>%mutate(
interpretation=ifelse( term!="(Intercept)",paste("Fixing all else constant, a unit increase in ",tolower(variable_name),", changes the odds of ",outcome," by: ", estimate_or, " (",conf.low_or, "-",conf.high_or,") on average. In other words, the odds of ",outcome," changes by", 100 * (estimate_or - 1), "% due to each unit increase in ",tolower(variable_name)),paste("Not meaningful but can be roughly translated to the odds of ",outcome," which is ",estimate_or, " (",conf.low_or, "-",conf.high_or,") ,fixing all else constant")),
interpretation=ifelse(categorical==T,paste("The odds of ",outcome," in ", tolower(variable_name), " is ", estimate_or, " (",conf.low_or, "-",conf.high_or,") times the odds of ", outcome," in the reference group, fixing all else constant." ),interpretation )
)%>%select(variable_name,interpretation )%>%
kable( "html", booktabs = T, longtable = F, digits=2,
caption = paste("Model Interpretation | ",capitalize(outcome)) ) %>%
kable_styling(bootstrap_options = c("striped","hold_position","condensed","responsive"))
}
generateLogitPlot= function(outcome,topn,model.df){
ggplot(model.df%>%top_n(topn,p.value), aes(estimate, variable_name, color = variable_name )) +
geom_point() + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) + geom_vline(xintercept = 1, color = "blue", lty = 2) +
ggtitle( sprintf( paste("Precision Comparison | ",capitalize(outcome)) ) )+ xlab("Log Odds Ratio") + ylab("Covariate")+
theme_economist()+ guides(fill=FALSE, color=FALSE)+theme(plot.title = element_text(size=14, face="bold"))
}
```
## READ and CLEAN
```{r warning=F}
dataset <- readxl::read_excel("merged.xlsx")%>%
# select(-`date_`)%>%
filter(!code_no %in% c("LB454","LB455","LB456")) %>%
mutate(across(c('lab_hct','cp_sbp', 'lab_wbc_count'), as.numeric)) %>%
mutate(across(where(is.character), str_trim)) %>%
mutate(across(where(is.character), capitalize)) %>%
mutate(
preg_outcome=`pregnancy outcome`,sickle_cell=`sickle Cell`,chlamydia=`Chlamydia`,gonorrhea=`Gonorrhea`,
birth_weight_grams=as.numeric(ifelse(code_no=="LB337",1950,birth_weight_grams)),
) %>%
purrr::modify_if( is.character, function (x) str_replace_all(x, c(
"\\-VE"="Negative","\\+VE"="Positive","Svd"="SVD",
"\\-Ve"="Negative","\\+Ve"="Positive","Error"=NA, "Invalid"=NA
)))%>%
mutate(low_birth_weight=factor(ifelse(birth_weight_grams<2500,"Yes", "No")),
# outcome=ifelse(preg_outcome=="LTFU", "LTFU", NA),
# outcome=factor(ifelse(is.na(outcome), "Completed",outcome)),
preg_loss=ifelse(preg_outcome=="Twins"|preg_outcome=="Preterm", "No",preg_outcome),
preg_loss=ifelse(is.na(preg_loss), "No",preg_loss),
preg_loss=ifelse(preg_loss=="LTFU", NA, preg_loss),
preg_loss=factor(ifelse(preg_loss=="No", "No", "Yes")),
still_birth_death=ifelse(preg_outcome=="FSB"|preg_outcome=="Died", "Yes",preg_outcome),
still_birth_death=ifelse(is.na(still_birth_death), "No",still_birth_death),
still_birth_death=ifelse(still_birth_death=="LTFU", NA,still_birth_death),
still_birth_death=factor(ifelse(still_birth_death=="Yes", "Yes", "No")),
preterm=ifelse(pregnancy_duration_weeks<=37 & preg_loss=="No", "Yes","No"),
preg_duration_cat=relevel(factor(ifelse(pregnancy_duration_weeks<=37, "<38 weeks",">=38 weeks")), "<38 weeks"),
outcome=ifelse(preg_loss=="Yes", "Pregnancy Loss", "Delivered"),
outcome=relevel(factor(ifelse(is.na(outcome), "LTFU",outcome)),"Pregnancy Loss"),
birth_weight_cat=relevel(factor(ifelse(birth_weight_grams<2500, "<2500 grams",">=2500 grams")), "<2500 grams"),
# SE
years_marriage=ifelse(cp_marital_status=="Married",sh_relationship_years,NA),
years_single=ifelse(cp_marital_status=="Single",sh_relationship_years,NA),
polygamous=ifelse(sh_relationship_type=="Polygamous","Yes","No"),
education_level=ifelse(se_education_level=="College"|se_education_level=="University","College/University",se_education_level),
education_level_cat=ifelse(education_level=="Primary"|education_level=="None","Primary and below","Secondary and above"),
# ANC
trimester_1st_anc=ifelse(cp_anc_1st_anc<=12,"First",NA),
trimester_1st_anc=ifelse( cp_anc_1st_anc>12&cp_anc_1st_anc<=24,"Second",trimester_1st_anc),
trimester_1st_anc=ifelse( cp_anc_1st_anc>24,"Third",trimester_1st_anc),
anc_hb_low=ifelse(gesthb<=12&ancp_hb_results<11.0,"Yes", NA),
anc_hb_low=ifelse(gesthb>24&ancp_hb_results<11.0,"Yes", anc_hb_low),
anc_hb_low=ifelse(gesthb>12&gesthb<=24&ancp_hb_results<10.5,"Yes", anc_hb_low),
anc_hb_low=relevel(factor(ifelse(is.na(anc_hb_low)&!is.na(ancp_hb_results), "No",anc_hb_low)),"Yes"),
hiv_haart_regimen=relevel(factor(ifelse(hiv_haart_regimen=="Yes","Yes","N/A")),"Yes"),
ancp_vdrl_rpr_results=relevel(factor(ifelse(ancp_vdrl_rpr_results=="NR","Negative",ancp_vdrl_rpr_results)),"Positive"),
ancp_hiv_results=relevel(factor(ifelse(ancp_hiv_results=="NR","Negative","Positive")),"Positive"),
# Lab
ferritin_cat=ifelse(ferritin<=15,"<= 15",NA),
ferritin_cat=ifelse( ferritin>16&ferritin<=30,"16 – 30",ferritin_cat),
ferritin_cat=ifelse( ferritin>30,">30",ferritin_cat),
lab_hb_low=ifelse(gesthb<=12&lab_hgb<11.0,"Yes", NA),
lab_hb_low=ifelse(gesthb>24&lab_hgb<11.0,"Yes", lab_hb_low),
lab_hb_low=ifelse(gesthb>12&gesthb<=24&lab_hgb<10.5,"Yes", lab_hb_low),
lab_hb_low=relevel(factor(ifelse(is.na(lab_hb_low)&!is.na(lab_hgb), "No",lab_hb_low)),"Yes"),
sickle_cell=relevel(factor(ifelse(sickle_cell=="FA","No","Yes")),"Yes"),
ancp_urinalysis_cat = case_when(
str_detect(ancp_urinalysis_results,"NAD") ~ "No abnormality",
ancp_urinalysis_results=="PH. 6.5" ~ "No abnormality",
str_detect(ancp_urinalysis_results,"Pus cells") ~ "Pus cells/leucocytes/nitrites ",
str_detect(ancp_urinalysis_results,"Epithelial cells") ~ "Contaminated",
is.na(ancp_urinalysis_results) ~ NA_character_,
TRUE ~ "Protein"),
ancp_uti=ifelse(str_detect(ancp_urinalysis_cat,"Pus cells"),"Yes","No"),
anemia_cat=ifelse(lab_hgb<7,"Severe anemia (HB<7)",NA),
anemia_cat=ifelse( lab_hgb>=7&lab_hgb<=10,"Moderate anemia (HB>=7 & HB<=10)",anemia_cat),
anemia_cat=ifelse( lab_hgb>10&lab_hgb<11,"Mild anemia (HB>10 & HB<11)",anemia_cat),
anemic=ifelse(lab_hgb<11,"Yes", "No"),
# household
pollutant_q8_fuel_cook = case_when(
q8_fuel_cook=="Kerosene"|q8_fuel_cook=="Propane gas" |q8_fuel_cook=="Charcoal"~ "Yes",
is.na(q8_fuel_cook) ~ NA_character_,
TRUE ~ "No"),
porous_q2_kitchen_wall_material = case_when(
q2_kitchen_wall_material=="Bamboo"|q2_kitchen_wall_material=="Thatch"~ "Yes",
is.na(q2_kitchen_wall_material) ~ NA_character_,
TRUE ~ "No"),
q3_kitchen_adobe_floor=ifelse(str_detect(q3_kitchen_floor_material,"Uncovered"),"Yes","No"),
q1_thatched_kitchen_roof=ifelse(str_detect(q1_kitchen_roof_material,"Thatch"),"Yes","No"),
# cat
age_cat=ifelse(age<18,"<18",NA),
age_cat=ifelse( age>=18&age<=25,"18 – 25",age_cat),
age_cat=ifelse( age>=26&age<=35,"26 – 35",age_cat),
age_cat=ifelse( age>35,">35",age_cat),
cp_bmi_cat=ifelse(cp_bmi<18.5,"<18.5",NA),
cp_bmi_cat=ifelse( cp_bmi>=18.5&cp_bmi<=24.9,"18.5 – 24.9",cp_bmi_cat),
cp_bmi_cat=ifelse( cp_bmi>=25&cp_bmi<=29.9,"25 – 29.9",cp_bmi_cat),
cp_bmi_cat=ifelse( cp_bmi>30,">30",cp_bmi_cat),
gravida_cat=ifelse(gravida==1,"1",NA),
gravida_cat=ifelse( gravida>=2&gravida<=4,"2 – 4",gravida_cat),
gravida_cat=ifelse( gravida>5,">5",gravida_cat),
age_less18=ifelse(age<18,"<18",">18"),
ferritin_less15=ifelse(ferritin<=15,"Yes","No"),
vaginosis_cat=ifelse(vaginosis=="Intermediate"|vaginosis=="BV","Positive","Negative"),
se_occupation_cat = case_when(
se_occupation=="Unemployed" ~ "Unemployed",
se_occupation=="Farmer"|se_occupation=="Self employed/ Businees" ~ "Self employed",
is.na(se_occupation) ~ NA_character_,
TRUE ~ "Employed"),
se_spouse_occupation_cat = case_when(
se_spouse_occupation=="Unemployed" ~ "Unemployed",
se_spouse_occupation=="Farmer"|se_spouse_occupation=="Self employed/ Businees" ~ "Self employed",
is.na(se_spouse_occupation) ~ NA_character_,
TRUE ~ "Employed"),
# house holds
q16_be_time_3rd_meal=as.numeric(ifelse(q16_be_time_3rd_meal=="7:00PM", NA, q16_be_time_3rd_meal)),
q3_kitchen_floor_material=ifelse(q3_kitchen_floor_material=="0", NA, q3_kitchen_floor_material),
# FIX
#inpouch=relevel( factor(ifelse(is.na(inpouch), "Missing",inpouch)), "Negative" ),
inpouch=ifelse(is.na(inpouch), "Missing",inpouch),
years_single=ifelse(code_no=="LB325",-0.1,years_single),
pregnancy_duration_weeks=ifelse(code_no=="LB325",-0.1,pregnancy_duration_weeks),
birth_weight_grams=ifelse(code_no=="LB325",-0.1,birth_weight_grams),
preg_outcomexx=preg_outcome
) %>% rowwise() %>%mutate(
num_prev_preg_loss=sum(ifelse(pp1_outcome=="Alive",0,1),ifelse(pp2_outcome=="Alive",0,1),ifelse(pp3_outcome=="Alive",0,1),
ifelse(pp4_outcome=="Alive",0,1),ifelse(pp5_outcome=="Alive",0,1),ifelse(pp6_outcome=="Alive",0,1),na.rm=T),
num_prev_abortions=sum(ifelse(pp1_outcome=="Abortion",1,0),ifelse(pp2_outcome=="Abortion",1,0),ifelse(pp3_outcome=="Abortion",1,0),
ifelse(pp4_outcome=="Abortion",1,0),ifelse(pp5_outcome=="Abortion",1,0),ifelse(pp6_outcome=="Abortion",1,0),na.rm=T),
num_prev_deaths=sum(ifelse(pp1_outcome=="Died",1,0),ifelse(pp2_outcome=="Died",1,0),ifelse(pp3_outcome=="Died",1,0),
ifelse(pp4_outcome=="Died",1,0),ifelse(pp5_outcome=="Died",1,0),ifelse(pp6_outcome=="Died",1,0),na.rm=T),
num_prev_still_births=sum(ifelse(pp1_outcome=="Still birth",1,0),ifelse(pp2_outcome=="Still birth",1,0),ifelse(pp3_outcome=="Still birth",1,0),
ifelse(pp4_outcome=="Still birth",1,0),ifelse(pp5_outcome=="Still birth",1,0),ifelse(pp6_outcome=="Still birth",1,0),na.rm=T),
num_prev_miscarriage=sum(ifelse(pp1_outcome=="Miscarriage",1,0),ifelse(pp2_outcome=="Miscarriage",1,0),ifelse(pp3_outcome=="Miscarriage",1,0),
ifelse(pp4_outcome=="Miscarriage",1,0),ifelse(pp5_outcome=="Miscarriage",1,0),ifelse(pp6_outcome=="Miscarriage",1,0),na.rm=T),
num_prev_alive_births=sum(ifelse(pp1_outcome=="Alive",1,0),ifelse(pp2_outcome=="Alive",1,0),ifelse(pp3_outcome=="Alive",1,0),
ifelse(pp4_outcome=="Alive",1,0),ifelse(pp5_outcome=="Alive",1,0),ifelse(pp6_outcome=="Alive",1,0),na.rm=T),
any_prev_preg_loss=ifelse(num_prev_preg_loss>0,"Yes","No"),
any_prev_abortions=ifelse(num_prev_abortions>0,"Yes","No"),
any_prev_deaths=ifelse(num_prev_deaths>0,"Yes","No"),
any_prev_still_births=ifelse(num_prev_still_births>0,"Yes","No"),
any_prev_miscarriage=ifelse(num_prev_miscarriage>0,"Yes","No"),
any_prev_alive_births=ifelse(num_prev_alive_births>0,"Yes","No"),
num_prev_lbw=sum(ifelse(pp1_birthweight_kgs<2500,1,0),ifelse(pp2_birthweight_kgs<2500,1,0),ifelse(pp3_birthweight_kgs<2500,1,0),
ifelse(pp4_birthweight_kgs<2500,1,0),ifelse(pp5_birthweight_kgs<2500,1,0),ifelse(pp6_birthweight_kgs<2500,1,0),na.rm=T),
any_prev_lbw=ifelse(num_prev_lbw>0,"Yes","No"),
num_prev_preterm=sum(ifelse(pp1_maturity_wks<=37,1,0),ifelse(pp2_maturity_wks<=37,1,0),ifelse(pp3_maturity_wks<=37,1,0),
ifelse(pp4_maturity_wks<=37,1,0),ifelse(pp5_maturity_wks<=37,1,0),ifelse(pp6_maturity_wks<=37,1,0),na.rm=T),
any_prev_preterm=ifelse(num_prev_preterm>0,"Yes","No"),
)
varList= list(
"Yes"=c('preg_loss','low_birth_weight','polygamous','anc_hb_low','hiv_haart_regimen','lab_hb_low', 'sickle_cell', "preterm", "still_birth_death", "ferritin_less15",
"any_prev_preg_loss","any_prev_abortions","any_prev_deaths","any_prev_still_births","any_prev_miscarriage","any_prev_alive_births","any_prev_lbw",
"any_prev_preterm", "ancp_uti", "anemic", "is_ipt_dose_given","is_taken_iron_supps","is_deworming_done","is_used_contraceptives","pollutant_q8_fuel_cook",
"porous_q2_kitchen_wall_material","q3_kitchen_adobe_floor","q1_thatched_kitchen_roof"
),
"Positive"=c('ancp_rhesus_results','ancp_vdrl_rpr_results','ancp_hiv_results','inpouch','lab_rdt','chlamydia','gonorrhea',"vaginosis_cat")
)
dataset= relevelBy(varList,dataset)
#sapply(dataset, class)
```
# Univariate Analysis
## Table 1: Demographics and socio-economic characteristics
```{r fig.align='center',warning=FALSE}
na.rm.var=F;stats=F
# Getting descriptive statistics
mergeDesc("Age (years)" = getTable1Stats("age","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Marital Status" = getTable1Stats("cp_marital_status","outcome",desc_both,statistics=stats,na.rm=na.rm.var),
"Years of marriage" =getTable1Stats("years_marriage","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Years of relationship (single)" = getTable1Stats("years_single","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Polygamous marriage" =getTable1Stats("polygamous","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Occupation (%)" = getTable1Stats("se_occupation_cat","outcome",desc_both,statistics=stats,na.rm=na.rm.var),
"Spouse Occupation (%)" = getTable1Stats("se_spouse_occupation_cat","outcome",desc_both,statistics=stats,na.rm=na.rm.var),
"Education (%)" =getTable1Stats("education_level","outcome",desc_median,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Demographics and socio-economic characteristics </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1,2,1 ), cgroup = c('', 'General Study Outcomes', '') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
## Table 2: Clinical and ANC profile
```{r fig.align='center',warning=FALSE}
na.rm.var=F;stats=F
# Getting descriptive statistics
mergeDesc("Weight (Kg)" = getTable1Stats("cp_weight","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Height (cm)" = getTable1Stats("cp_height","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"BMI (Kg/m2)" =getTable1Stats("cp_bmi","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Systolic BP (mm Hg)" = getTable1Stats("cp_sbp","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Diastolic BP (mm Hg) " =getTable1Stats("cp_dbp","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Gestation at enrollment (Weeks)" = getTable1Stats("cp_gbd","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Gravidity" =getTable1Stats("gravida","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Number of ANC visits (%)" = getTable1Stats("cp_anc_attendance","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Gestation at first ANC attendance (Weeks)" = getTable1Stats("cp_anc_1st_anc","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Trimester at first ANC attendance (%)" = getTable1Stats("trimester_1st_anc","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Hemoglobin (g/dL) (ANC)" = getTable1Stats("ancp_hb_results","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"HB <11.0 at 1st & 3rd trimesters & <10.5 at 2nd trimester" = getTable1Stats("anc_hb_low","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"GBD at HB measurement (Weeks)" =getTable1Stats("gesthb","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Blood Group (%)" = getTable1Stats("ancp_bloodgroup_results","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Rhesus" = getTable1Stats("ancp_rhesus_results","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"VDRL" = getTable1Stats("ancp_vdrl_rpr_results","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"HIV" = getTable1Stats("ancp_hiv_results","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"On HAART " = getTable1Stats("hiv_haart_regimen","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Urinalysis" = getTable1Stats("ancp_urinalysis_cat","outcome",desc_median,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Clinical and ANC profile</b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1, 2 ), cgroup = c('', 'General Study Outcomes', '') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
## Table 3: Previous pregnancy characteristics
```{r fig.align='center',warning=FALSE}
na.rm.var=F;stats=F
# Getting descriptive statistics
mergeDesc(
"Previous preterms" = getTable1Stats("any_prev_preterm","outcome",desc_both,statistics=stats,na.rm=na.rm.var),
"Previous low birth weight [n, (%)]" = getTable1Stats("any_prev_lbw","outcome",desc_both,statistics=stats,na.rm=na.rm.var),
"Previous miscarriages [n, (%)]" = getTable1Stats("any_prev_miscarriage","outcome",desc_both,statistics=stats,na.rm=na.rm.var),
"Previous still births [n, (%)]" = getTable1Stats("any_prev_still_births","outcome",desc_both,statistics=stats,na.rm=na.rm.var),
"Previous deaths [n, (%)]" = getTable1Stats("any_prev_deaths","outcome",desc_both,statistics=stats,na.rm=na.rm.var),
"Previous abortions [n, (%)]" = getTable1Stats("any_prev_abortions","outcome",desc_both,statistics=stats,na.rm=na.rm.var),
"Previous pregnancy loss [n, (%)]" = getTable1Stats("any_prev_preg_loss","outcome",desc_both,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Previous pregnancy characteristics</b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1, 2 ), cgroup = c('', 'General Study Outcomes', '') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
## Table 4: Laboratory results
```{r fig.align='center',warning=FALSE}
na.rm.var=F;stats=F
# Getting descriptive statistics
mergeDesc("Ferritin (µg/L)" = getTable1Stats("ferritin","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Ferritin (µg/L) (%)" = getTable1Stats("ferritin_cat","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Hemoglobin (g/dL) (Lab)" = getTable1Stats("lab_hgb","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"HB <11.0 at 1st & 3rd trimesters & <10.5 at 2nd trimester (%)" =getTable1Stats("lab_hb_low","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"HCT" = getTable1Stats("lab_hct","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"White Blood cell count" =getTable1Stats("lab_wbc_count","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"MCV " = getTable1Stats("lab_mcv","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Sickle cell carriers" =getTable1Stats("sickle_cell","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Trichonomiasis " =getTable1Stats("inpouch","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Vaginosis" =getTable1Stats("vaginosis","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Malaria " =getTable1Stats("lab_rdt","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Chlamydia " =getTable1Stats("chlamydia","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Gonorrhea " =getTable1Stats("gonorrhea","outcome",desc_median,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Laboratory results </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1, 2 ), cgroup = c('', 'General Study Outcomes', ''),tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
## Table 5: Pregnancy outcomes
```{r warning=FALSE}
na.rm.var=F;stats=F
# Getting descriptive statistics
mergeDesc("Pregnancy duration (weeks)" = getTable1Stats("pregnancy_duration_weeks","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Pregnancy duration (%)" = getTable1Stats("preg_duration_cat","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Still Birth or death" = getTable1Stats("still_birth_death","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Delivery mode (%)" =getTable1Stats("delivery_mode","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Birth weight (grams)" = getTable1Stats("birth_weight_grams","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Birth weight (%)" =getTable1Stats("birth_weight_cat","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Delivery place (%) " = getTable1Stats("delivery_place","outcome",desc_median,statistics=stats,na.rm=na.rm.var),
"Delivery by (%)" =getTable1Stats("delivery_conducted_by","outcome",desc_median,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Pregnancy outcomes </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1, 2 ), cgroup = c('', 'General Study Outcomes', ''), tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
## Table 6: Associations with pregnancy loss
```{r fig.align='center',warning=FALSE}
na.rm.var=T;stats=T
mergeDesc(
"Maternal age (Median (IQR))" = getTable1Stats("age","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Maternal age (%)" = getTable1Stats("age_cat","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Years of marriage (Median (IQR))" =getTable1Stats("years_marriage","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"BMI (Median (IQR))" = getTable1Stats("cp_bmi","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"BMI (%)" = getTable1Stats("cp_bmi_cat","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Previous low birth weight [n, (%)]" = getTable1Stats("any_prev_lbw","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Previous abortion " = getTable1Stats("any_prev_abortions","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Previous pregnancy loss [n, (%)]" = getTable1Stats("any_prev_preg_loss","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Gestation at enrollment (Weeks)" = getTable1Stats("cp_gbd","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Gravida (%)" =getTable1Stats("gravida_cat","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Iron deficiency ( Ferritin <= 15 )" = getTable1Stats("ferritin_less15","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Anemic " = getTable1Stats("anemic","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Sickle cell disease" =getTable1Stats("sickle_cell","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Trichonomiasis" =getTable1Stats("inpouch","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Vaginosis" =getTable1Stats("vaginosis_cat","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Chlamydia " =getTable1Stats("chlamydia","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Gonorrhea " =getTable1Stats("gonorrhea","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Malaria " =getTable1Stats("lab_rdt","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"HIV " =getTable1Stats("ancp_hiv_results","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
# "#TODO#Any acute infection " =getTable1Stats("gonorrhea","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),#TODO
"Urinary tract infection" =getTable1Stats("ancp_uti","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Syphilis screening" =getTable1Stats("ancp_vdrl_rpr_results","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Anemia" =getTable1Stats("anemia_cat","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Associations with pregnancy loss </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1, 2,1 ), cgroup = c('', 'Pregnancy Loss', '') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
## Table 7: Associations with low birth weight
```{r fig.align='center',warning=FALSE}
tests=dataset%>%filter(ancp_vdrl_rpr_results=="Positive")
na.rm.var=T;stats=T
# Getting descriptive statistics
mergeDesc(#"Maternal age (Median (IQR))" = getTable1Stats("age","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Maternal age (%)" = getTable1Stats("age_cat","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Years of marriage (Median (IQR))" =getTable1Stats("years_marriage","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
#"BMI (Median (IQR))" = getTable1Stats("cp_bmi","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"BMI (%)" = getTable1Stats("cp_bmi_cat","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Previous low birth weight [n, (%)]" = getTable1Stats("any_prev_lbw","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Previous deaths [n, (%)]" = getTable1Stats("any_prev_deaths","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Previous pregnancy loss [n, (%)]" = getTable1Stats("any_prev_preg_loss","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Gestation at enrollment (Weeks)" = getTable1Stats("cp_gbd","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Gravida (%)" =getTable1Stats("gravida_cat","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Iron deficiency ( Ferritin <= 15 )" = getTable1Stats("ferritin_less15","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Anemic " = getTable1Stats("anemic","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Sickle cell disease" =getTable1Stats("sickle_cell","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Trichonomiasis" =getTable1Stats("inpouch","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Vaginosis" =getTable1Stats("vaginosis_cat","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Chlamydia " =getTable1Stats("chlamydia","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Gonorrhea " =getTable1Stats("gonorrhea","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Malaria " =getTable1Stats("lab_rdt","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"HIV " =getTable1Stats("ancp_hiv_results","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
#"#TODO#Any acute infection " =getTable1Stats("gonorrhea","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),#TODO
"Urinary tract infection" =getTable1Stats("ancp_uti","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Syphilis screening" =getTable1Stats("ancp_vdrl_rpr_results","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var),
"Anemia" =getTable1Stats("anemia_cat","low_birth_weight",desc_median,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Associations with low birth weight </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1, 2,1 ), cgroup = c('', 'Low Birth Weight', '') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
## Table 9: Association with household pollutants
```{r fig.align='center',warning=FALSE}
# Getting descriptive statistics
na.rm.var=F;stats=T
mergeDesc(
"Thatched kitchen roof" = getMultipleTable1Stats("q1_thatched_kitchen_roof","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
#"kitchen roofing material" = getMultipleTable1Stats("q1_kitchen_roof_material","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"porous kitchen wall material"= getMultipleTable1Stats("porous_q2_kitchen_wall_material","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
#"kitchen walling material" = getMultipleTable1Stats("q2_kitchen_wall_material","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"kitchen adobe floor" = getMultipleTable1Stats("q3_kitchen_adobe_floor","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"kitchen permanent entryways" = getMultipleTable1Stats("q5_kitchen_permanent_entryways","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"permanent open windows (n)" = getMultipleTable1Stats("q7_no_permanent_windows_open","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
#"fuel used in cooking" = getMultipleTable1Stats("q8_fuel_cook","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"pollutant fuel"= getMultipleTable1Stats("pollutant_q8_fuel_cook","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"months cook house" = getMultipleTable1Stats("q10_months_cook_house","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"age started cooking (yrs)" = getMultipleTable1Stats("q12_be_ageyrs_start_cook","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"years spent cooking" = getMultipleTable1Stats("q14_be_years_spent_cooking","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
#"currently smoking" = getMultipleTable1Stats("q18_currently_smoke","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"age started smoking" = getMultipleTable1Stats("q20_ageyrs_startsmoking","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"daily cigarettes (n)" = getMultipleTable1Stats("q22_no_cig_smoke","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"anyone smoking in house" = getMultipleTable1Stats("q24_anyone_smoke_house","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var)
) %>%
htmlTable( caption = "<b> Association with household pollutants </b>",useViewer=T,ctable = TRUE, align = 'ccccc',
n.cgroup = c(2,1,2,1,2,1 ), cgroup = c( 'Low Birth Weight','','Preterm','','Pregnancy Loss', '')
,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
## Table 9: Correlates of birth outcomes
```{r fig.align='center',warning=FALSE}
# Getting descriptive statistics
na.rm.var=F;stats=T
mergeDesc(
"Marital status" = getMultipleTable1Stats("cp_marital_status","low_birth_weight","preterm","preg_loss",desc_both,statistics=stats,na.rm=na.rm.var),
"BMI" = getMultipleTable1Stats("cp_bmi_cat","low_birth_weight","preterm","preg_loss",desc_both,statistics=stats,na.rm=na.rm.var),
"Booking trimester" = getMultipleTable1Stats("trimester_1st_anc","preterm","low_birth_weight","preg_loss",desc_both,statistics=stats,na.rm=na.rm.var),
"Education level" =getMultipleTable1Stats("education_level","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Occupation" = getMultipleTable1Stats("se_occupation_cat","low_birth_weight","preterm","preg_loss",desc_both,statistics=stats,na.rm=na.rm.var),
"Spouse Occupation (%)" = getMultipleTable1Stats("se_spouse_occupation_cat","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"IPT" = getMultipleTable1Stats("is_ipt_dose_given","low_birth_weight","preterm","preg_loss",desc_both,statistics=stats,na.rm=na.rm.var),
"Iron Supplementation" =getMultipleTable1Stats("is_taken_iron_supps","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Deworming" =getMultipleTable1Stats("is_deworming_done","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var),
"Contraceptive use" =getMultipleTable1Stats("is_used_contraceptives","low_birth_weight","preterm","preg_loss",desc_median,statistics=stats,na.rm=na.rm.var)
) %>%
htmlTable( caption = "<b> Correlates of birth outcomes </b>",useViewer=T,ctable = TRUE, align = 'ccccc',
n.cgroup = c(2,1,2,1,2,1 ), cgroup = c( 'Low Birth Weight','','Preterm','','Pregnancy Loss', '')
,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
\newpage
# Missing Data Analysis
```{r fig.align='center', fig.width=5, warning=FALSE}
#
y <- "low_birth_weight"
x <- c("age","cp_marital_status","se_occupation_cat","education_level","cp_weight","cp_height","cp_bmi","cp_sbp",
"cp_dbp","cp_gbd","gravida","cp_anc_attendance","cp_anc_1st_anc",
"trimester_1st_anc","ancp_hb_results","anc_hb_low","gesthb",
"ancp_bloodgroup_results","ancp_rhesus_results","ancp_vdrl_rpr_results",
"ancp_hiv_results","hiv_haart_regimen","ancp_urinalysis_cat",
"any_prev_preterm","any_prev_lbw","any_prev_miscarriage",
"any_prev_still_births","any_prev_deaths","any_prev_abortions",
"any_prev_preg_loss","ferritin","ferritin_cat","lab_hgb",
"lab_hb_low","lab_hct","lab_wbc_count","lab_mcv","sickle_cell",
"inpouch","vaginosis","lab_rdt","chlamydia","gonorrhea",
"is_ipt_dose_given","is_taken_iron_supps","is_deworming_done","is_used_contraceptives",
"delivery_mode","delivery_place",#"delivery_conducted_by",
#"birth_weight_grams","birth_weight_cat",
# "pregnancy_duration_weeks","preg_duration_cat","still_birth_death",
"age_cat","cp_bmi_cat","gravida_cat","ferritin_less15","anemic",
"vaginosis_cat","ancp_uti",
"q1_thatched_kitchen_roof","porous_q2_kitchen_wall_material","q3_kitchen_adobe_floor","q5_kitchen_permanent_entryways",
"q7_no_permanent_windows_open","pollutant_q8_fuel_cook","q10_months_cook_house","q12_be_ageyrs_start_cook","q14_be_years_spent_cooking",
"q24_anyone_smoke_house"#,"q20_ageyrs_startsmoking","q22_no_cig_smoke",
)
kable( dataset%>%select(c(x,y)) %>%naniar::miss_var_summary()%>%top_n(20,n_miss))
naniar::gg_miss_var(dataset%>%select(c(x,y)) )+ labs(title = "Frequency of Missingness")
naniar::gg_miss_upset(dataset%>%select(x), nsets = 10,nintersects = NA)
naniar::gg_miss_fct(x = dataset%>%select(c(x,y)), fct = low_birth_weight)
no.na.data <- na.omit(dataset[c(y, x)])
# f <- as.formula(paste(y, paste(x, collapse = " + "), sep = " ~ "))
# lbw.logit <- glm(f, family="binomial", data=no.na.data) %>% stepAIC(trace = FALSE)
# summary(lbw.logit)
```
# Multivariate Complete Case Analysis
```{r fig.align='center',warning=TRUE, cache=F}
varList= list(
"No"=c('preg_loss','low_birth_weight','polygamous','anc_hb_low','lab_hb_low', 'sickle_cell', "preterm", "still_birth_death", "ferritin_less15",
"any_prev_preg_loss","any_prev_abortions","any_prev_deaths","any_prev_still_births","any_prev_miscarriage","any_prev_alive_births","any_prev_lbw",
"any_prev_preterm", "ancp_uti", "anemic", "is_ipt_dose_given","is_taken_iron_supps","is_deworming_done","is_used_contraceptives","pollutant_q8_fuel_cook",
"porous_q2_kitchen_wall_material","q3_kitchen_adobe_floor","q1_thatched_kitchen_roof"
),
"Negative"=c('ancp_rhesus_results','ancp_vdrl_rpr_results','ancp_hiv_results','inpouch','lab_rdt','chlamydia','gonorrhea',"vaginosis_cat"),
"Unemployed"=c("se_occupation_cat")
)
dataset= relevelBy(varList,dataset)
```
## Low Birth Weight (LBW) Logistic Regression Model
```{r fig.align='center',warning=TRUE, cache=F, results="asis"}
## params
outcome="low birth weight"
topn=-7
variable_name=c("Intercept",'Age',"BMI", "Gestation", "Positive syphilis screening ", "Positive Iron deficiency ( Ferritin <= 15 )", "Employed","Self employed", "Pregnancy duration (>=38 weeks)", "Low hemoglobin", "Education level ( >= Secondary)", "Gravidity", "Previous deaths", "Pollutant fuel", "Taking iron supplement")
categorical=c(F,F,F,F,T,T,T,T,T,T,T,F,F,T,T)
# model
lbw.logit <- glm(low_birth_weight~ age +cp_bmi+cp_gbd+ancp_vdrl_rpr_results+ferritin_less15+se_occupation_cat+
preg_duration_cat+lab_hb_low+education_level_cat+
gravida+num_prev_deaths+pollutant_q8_fuel_cook+is_taken_iron_supps, family="binomial", data=dataset)
#summary(lbw.logit)
stargazer2(lbw.logit,
title="Title: Logistic Results",
dep.var.caption = "Odds Ratio (CI)", df = FALSE, header = FALSE, digits=2,
align=TRUE,intercept.bottom =F,
type = "html",single.row = T,
#style = "ajs", # "ajs"
ci = T,odd.ratio = T,
notes="Confidence Interval in parenthesis",
covariate.labels=variable_name
)
```
```{r fig.align='center',warning=TRUE, cache=F}
t2=cbind(OR = exp(coef(lbw.logit)), exp(confint(lbw.logit)), `P-value` = coef(lbw.logit)[2])
#kable(t2)
model.df = broom::tidy(lbw.logit, conf.int = TRUE)%>%
mutate(
estimate_or=round(exp(estimate),2),
conf.low_or=round(exp(conf.low),2),
conf.high_or=round(exp(conf.high),2),
variable_name=variable_name,
categorical=categorical
)
generateInterpretation(outcome,topn,model.df)
generateLogitPlot(outcome,topn,model.df)
```
## Pregnancy Loss (PL) Logistic Regression Model
```{r fig.align='center',warning=F, cache=F, results="asis"}
## params
outcome="pregnancy loss"
topn=-7
variable_name=c("Intercept",'Age',"BMI", "Gestation", "Urinary tract infection", "Positive Iron deficiency ( Ferritin <= 15 )", "Employed","Self employed", "Pregnancy duration (>=38 weeks)", "Low hemoglobin", "Education level ( >= Secondary)", "Gravidity", "Vaginosis", "Pollutant fuel", "Taking iron supplement")
categorical=c(F,F,F,F,T,T,T,T,T,T,T,F,F,T,T)
# model
pl.logit <- glm(preg_loss~ age +cp_bmi+cp_gbd+ancp_uti+ferritin_less15+se_occupation_cat+
preg_duration_cat+lab_hb_low+education_level_cat+
gravida+vaginosis_cat+pollutant_q8_fuel_cook+is_taken_iron_supps, family="binomial", data=dataset)
#summary(pl.logit)
stargazer2(pl.logit,
title="Title: Logistic Results",
dep.var.caption = "Odds Ratio (CI)", df = FALSE, header = FALSE, digits=2,
align=TRUE,intercept.bottom =F,
type = "html",single.row = T,
#style = "ajs", # "ajs"
ci = T,odd.ratio = T,
notes="Confidence Interval in parenthesis",
covariate.labels=variable_name
)
```
```{r fig.align='center',warning=TRUE, cache=F}
t2=cbind(OR = exp(coef(pl.logit)), exp(confint(pl.logit)), `P-value` = coef(pl.logit)[2])
#kable(t2)
model.df = broom::tidy(pl.logit, conf.int = TRUE)%>%
mutate(
estimate_or=round(exp(estimate),2),
conf.low_or=round(exp(conf.low),2),
conf.high_or=round(exp(conf.high),2),
variable_name=variable_name,
categorical=categorical
)
generateInterpretation(outcome,topn,model.df)
generateLogitPlot(outcome,topn,model.df)
```
## LBW and PL Summary
```{r fig.align='center',warning=F, cache=F, results="asis"}
variable_name=c("Intercept",'Age',"BMI", "Gestation", "Urinary tract infection", "Positive syphilis screening","Positive Iron deficiency ( Ferritin <= 15 )", "Employed","Self employed", "Pregnancy duration (>=38 weeks)", "Low hemoglobin", "Education level ( >= Secondary)", "Gravidity", "Vaginosis", "Previous deaths", "Pollutant fuel", "Taking iron supplement")
stargazer2(list("pregnancy loss"=pl.logit,"low birth weight"=lbw.logit),
title="Title: Logistic Results",
dep.var.caption = "Odds Ratio (CI)", df = FALSE, header = FALSE, digits=2,
align=TRUE,intercept.bottom =F,
type = "html",single.row = T,
#style = "ajs", # "ajs"
ci = T,odd.ratio = T,
notes="Confidence Interval in parenthesis",
covariate.labels=variable_name
)
```
\newpage
# Appendix
## Explaratory Data Analysis | EDA
### Bivariate Analysis
To understand the relationship of each continuous variable with the y variable:
1. Plot box plot for each of the variables to do a visual comparison between the groups
2. Plot the explanatory variable distribution for both the variables to understand the variability uniquely explained (The non-intersecting part of the blue and the red is the variation explained by the variable)
```{r fig.align='center', cache=T, fig.width=5, warning=FALSE}
y <- c("low_birth_weight","outcome", "preg_loss", "preterm")
x <- c("age","cp_marital_status","se_occupation_cat","education_level","cp_weight","cp_height","cp_bmi","cp_sbp",
"cp_dbp","cp_gbd","gravida","cp_anc_attendance","cp_anc_1st_anc",
"trimester_1st_anc","ancp_hb_results","anc_hb_low","gesthb",
"ancp_bloodgroup_results","ancp_rhesus_results","ancp_vdrl_rpr_results",
"ancp_hiv_results","hiv_haart_regimen","ancp_urinalysis_cat",
"any_prev_preterm","any_prev_lbw","any_prev_miscarriage",
"any_prev_still_births","any_prev_deaths","any_prev_abortions",
"any_prev_preg_loss","ferritin","ferritin_cat","lab_hgb",
"lab_hb_low","lab_hct","lab_wbc_count","lab_mcv","sickle_cell",
"inpouch","vaginosis","lab_rdt","chlamydia","gonorrhea",
"is_ipt_dose_given","is_taken_iron_supps","is_deworming_done","is_used_contraceptives",
"delivery_mode","delivery_place","delivery_conducted_by",
"birth_weight_grams","birth_weight_cat",
"pregnancy_duration_weeks","preg_duration_cat","still_birth_death",
"age_cat","cp_bmi_cat","gravida_cat","ferritin_less15","anemic",
"vaginosis_cat","ancp_uti",
"q1_thatched_kitchen_roof","porous_q2_kitchen_wall_material","q3_kitchen_adobe_floor","q5_kitchen_permanent_entryways",
"q7_no_permanent_windows_open","pollutant_q8_fuel_cook","q10_months_cook_house","q12_be_ageyrs_start_cook","q14_be_years_spent_cooking",
"q24_anyone_smoke_house","q20_ageyrs_startsmoking","q22_no_cig_smoke"
)
raw_df=dataset%>%select(c(y,x))
cont_univ_df <- raw_df %>% select_if(is.numeric) %>% dplyr::mutate(row_no =1:n() )
for (value in colnames(cont_univ_df[-ncol(cont_univ_df)])) {
print(value)
p1 <- ggplot2.histogram(raw_df%>%drop_na(low_birth_weight), xName=value, backgroundColor="white", bins=10,
#brewerPalette="Paired",
groupName="low_birth_weight", legendPosition="top", xScale="log2",
alpha=0.5, addDensity=TRUE, removePanelGrid=TRUE ,removePanelBorder=TRUE,
addMeanLine=TRUE, meanLineSize=1)+ theme_minimal()+ theme(legend.position="top")
p2<- ggboxplot(raw_df%>%drop_na(low_birth_weight), x = "low_birth_weight", y = value, color = "low_birth_weight", palette = "jco", add = "jitter")+
stat_compare_means()+ guides(fill=FALSE, color=FALSE) + theme_minimal()
p3 <- ggplot2.histogram(raw_df%>%drop_na(preg_loss), xName=value, backgroundColor="white", bins=10,
#brewerPalette="Paired",
groupName="preg_loss", legendPosition="top", xScale="log2",
alpha=0.5, addDensity=TRUE, removePanelGrid=TRUE ,removePanelBorder=TRUE,
addMeanLine=TRUE, meanLineSize=1)+ theme_minimal()+ theme(legend.position="top")
p4<- ggboxplot(raw_df%>%drop_na(preg_loss), x = "preg_loss", y = value, color = "preg_loss", palette = "jco", add = "jitter")+
stat_compare_means()+ guides(fill=FALSE, color=FALSE) + theme_minimal()
grid.arrange( p1,p2, p3,p4,nrow=2)
# print(p2 )
}