NHANES_depression/06_descriptives.R at main · Tirgit/NHANES_depression · GitHub

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# Set working directory
setwd("~/GitHub/NHANES_depression/Data")

# load libraries
# Load needed libraries
library(tidyverse)
library(survey)
library(tableone)

# Create Rubin's Rules functions
# Rubin's Rules - pooling means
rubin_mean <- function(average) {
  Reduce("+", average) / length(average)
}

# Rubin's Rules - pooling SEs
rubin_se <- function(average, standard_error) {
  # Within imputation variance:
  within_var <- Reduce("+", lapply(standard_error, function(i){i*i})) / length(standard_error)
  # Between imputation variance:
  between_var <- Reduce("+", lapply(average, function(i){(i-rubin_mean(average))*(i-rubin_mean(average))})) / (length(average)-1)
  between_var2 <- between_var/ length(average)
  # Total variance:
  total_var <- within_var+between_var+between_var2
  # Pooled SE:
  sqrt(total_var)
}

# result collection dataframe
means_list <- list()
ses_list <- list()
cat_list <- list()


# DESCRIPTIVES OVERALL

for (i in 1:15) {
  filename <- paste0("cluster_merged_",i,".rds")
  df <- readRDS(filename)

  # survey design
  nhanes.y <- svydesign(data=df, id=~SDMVPSU, strata=~SDMVSTRA, weights=~survey_weight, nest=TRUE)

  # subsetting data
  sub.y <- subset(nhanes.y, (df$depressed == 1))

  # table creation
  k <- tableone::svyCreateTableOne(vars = c('age', "BMI", "PA_sedentary",
                                            "PA_moderate", "PA_vigorous",
                                            "DPQ_total",
                                            'gender', "marital",
                                            "education", "ethnicity",
                                            "ann_household_income",
                                            "family_pov_level",
                                            "asthma", "arthritis",
                                            "heart_failure", "CHD",
                                            "angina", "heart_attack",
                                            "stroke", "bronchitis",
                                            "liver_condition", "thyroid_condition",
                                            "cancer",
                                            "diabetes", "hypertension",
                                            "hyperlipidemia" ,
                                            "smoking", "alcohol",
                                            "dep_category"),
                                   data = sub.y,
                                   includeNA = T,
                                   test = F, addOverall = F)

  res_means <- as.matrix(k$ContTable[[1]][,4])
  res_ses <- as.matrix(k$ContTable[[1]][,5])/sqrt(nrow(sub.y))
  means_list[[i]] <- res_means
  ses_list[[i]] <- res_ses
  cat_list[[i]] <- c(
    k$CatTable[[1]][[1]][,6],
    k$CatTable[[1]][[2]][,6],
    k$CatTable[[1]][[3]][,6],
    k$CatTable[[1]][[4]][,6],
    k$CatTable[[1]][[5]][,6],
    k$CatTable[[1]][[6]][,6],
    k$CatTable[[1]][[7]][,6],
    k$CatTable[[1]][[8]][,6],
    k$CatTable[[1]][[9]][,6],
    k$CatTable[[1]][[10]][,6],
    k$CatTable[[1]][[11]][,6],
    k$CatTable[[1]][[12]][,6],
    k$CatTable[[1]][[13]][,6],
    k$CatTable[[1]][[14]][,6],
    k$CatTable[[1]][[15]][,6],
    k$CatTable[[1]][[16]][,6],
    k$CatTable[[1]][[17]][,6],
    k$CatTable[[1]][[18]][,6],
    k$CatTable[[1]][[19]][,6],
    k$CatTable[[1]][[20]][,6],
    k$CatTable[[1]][[21]][,6],
    k$CatTable[[1]][[22]][,6],
    k$CatTable[[1]][[23]][,6])

}

means_survey <- rubin_mean(average = means_list)
SDs_survey <- rubin_se(average = means_list, standard_error = ses_list)*sqrt(nrow(sub.y))
cat_survey <- rubin_mean(average = cat_list)

c(round(means_survey,1))
c(round(SDs_survey,1))
c(round(cat_survey,1)[2], # sex: female
round(cat_survey,1)[3:8], # marital
round(cat_survey,1)[9:13], # education
round(cat_survey,1)[14:17], # ethnicity
round(cat_survey,1)[18:20], # income
round(cat_survey,1)[21:23], # family POV
round(cat_survey,1)[24], # asthma: yes
round(cat_survey,1)[26], # arthritis : yes
round(cat_survey,1)[28], # heart failure : yes
round(cat_survey,1)[30], # CHD : yes
round(cat_survey,1)[32], # angina : yes
round(cat_survey,1)[34], # heart attack  : yes
round(cat_survey,1)[36], # stroke  : yes
round(cat_survey,1)[38], # bronchitis  : yes
round(cat_survey,1)[40], # liver_condition  : yes
round(cat_survey,1)[42], # thyroid_condition  : yes
round(cat_survey,1)[44], # cancer  : yes
round(cat_survey,1)[46], # diabetes : yes
round(cat_survey,1)[49], # hypertension  : yes
round(cat_survey,1)[51], # hypercholesterolemia  : yes
round(cat_survey,1)[53:56], # smoking  : never, ex, sometimes, current
round(cat_survey,1)[57:59], # alcohol : never, light, heavy
round(cat_survey,1)[60:62]) # depression category: mod, mod-sev, sev


# DESCRIPTIVES PER CLUSTER

# result collection dataframe
means_list <- list()
ses_list <- list()
cat_list <- list()


# loading data
for (i in 1:15) {
  filename <- paste0("cluster_merged_",i,".rds")
  df <- readRDS(filename)

  # survey design
  nhanes.y <- svydesign(data=df, id=~SDMVPSU, strata=~SDMVSTRA, weights=~survey_weight, nest=TRUE)

  # subsetting data
  sub.y <- subset(nhanes.y, (df$cluster_membership == "6"))

  # table creation
  k <- tableone::svyCreateTableOne(vars = c('age', "BMI", "PA_sedentary",
                                            "PA_moderate", "PA_vigorous",
                                            "DPQ_total",
                                            'gender', "marital",
                                            "education", "ethnicity",
                                            "ann_household_income",
                                            "family_pov_level",
                                            "asthma", "arthritis",
                                            "heart_failure", "CHD",
                                            "angina", "heart_attack",
                                            "stroke", "bronchitis",
                                            "liver_condition", "thyroid_condition",
                                            "cancer",
                                            "diabetes", "hypertension",
                                            "hyperlipidemia" ,
                                            "smoking", "alcohol",
                                            "dep_category"),
                                   data = sub.y,
                                   includeNA = T,
                                   test = F, addOverall = F)

  res_means <- as.matrix(k$ContTable[[1]][,4])
  res_ses <- as.matrix(k$ContTable[[1]][,5])/sqrt(nrow(sub.y))
  means_list[[i]] <- res_means
  ses_list[[i]] <- res_ses
  cat_list[[i]] <- c(
    k$CatTable[[1]][[1]][,6],
    k$CatTable[[1]][[2]][,6],
    k$CatTable[[1]][[3]][,6],
    k$CatTable[[1]][[4]][,6],
    k$CatTable[[1]][[5]][,6],
    k$CatTable[[1]][[6]][,6],
    k$CatTable[[1]][[7]][,6],
    k$CatTable[[1]][[8]][,6],
    k$CatTable[[1]][[9]][,6],
    k$CatTable[[1]][[10]][,6],
    k$CatTable[[1]][[11]][,6],
    k$CatTable[[1]][[12]][,6],
    k$CatTable[[1]][[13]][,6],
    k$CatTable[[1]][[14]][,6],
    k$CatTable[[1]][[15]][,6],
    k$CatTable[[1]][[16]][,6],
    k$CatTable[[1]][[17]][,6],
    k$CatTable[[1]][[18]][,6],
    k$CatTable[[1]][[19]][,6],
    k$CatTable[[1]][[20]][,6],
    k$CatTable[[1]][[21]][,6],
    k$CatTable[[1]][[22]][,6],
    k$CatTable[[1]][[23]][,6])

}

means_survey <- rubin_mean(average = means_list)
SDs_survey <- rubin_se(average = means_list, standard_error = ses_list)*sqrt(nrow(sub.y))
cat_survey <- rubin_mean(average = cat_list)

nrow(sub.y)
c(round(means_survey,1))
c(round(SDs_survey,1))
c(round(cat_survey,1)[2], # sex: female
  round(cat_survey,1)[3:8], # marital
  round(cat_survey,1)[9:13], # education
  round(cat_survey,1)[14:17], # ethnicity
  round(cat_survey,1)[18:20], # income
  round(cat_survey,1)[21:23], # family POV
  round(cat_survey,1)[24], # asthma: yes
  round(cat_survey,1)[26], # arthritis : yes
  round(cat_survey,1)[28], # heart failure : yes
  round(cat_survey,1)[30], # CHD : yes
  round(cat_survey,1)[32], # angina : yes
  round(cat_survey,1)[34], # heart attack  : yes
  round(cat_survey,1)[36], # stroke  : yes
  round(cat_survey,1)[38], # bronchitis  : yes
  round(cat_survey,1)[40], # liver_condition  : yes
  round(cat_survey,1)[42], # thyroid_condition  : yes
  round(cat_survey,1)[44], # cancer  : yes
  round(cat_survey,1)[46], # diabetes : yes
  round(cat_survey,1)[49], # hypertension  : yes
  round(cat_survey,1)[51], # hypercholesterolemia  : yes
  round(cat_survey,1)[53:56], # smoking  : never, ex, sometimes, current
  round(cat_survey,1)[57:59], # alcohol : never, light, heavy
  round(cat_survey,1)[60:62]) # depression category: mod, mod-sev, sev


# COMPARING NUMERIC PER CLUSTER

varlist <- c("age", "BMI", "PA_sedentary", "PA_moderate", "PA_vigorous", "DPQ_total")
for (variable_in_loop in varlist) {
  # P value collection
  p_list <- list()
for (i in 1:15) {
  filename <- paste0("cluster_merged_",i,".rds")
  df <- readRDS(filename)

  # survey design
  nhanes.y <- svydesign(data=df, id=~SDMVPSU, strata=~SDMVSTRA, weights=~survey_weight, nest=TRUE)

  # subsetting data
  sub.y <- subset(nhanes.y, (df$depressed == 1))

  # ANOVA
  pasted_formula <- paste0(variable_in_loop, " ~ cluster_membership")
  form_loop <- as.formula(pasted_formula)
  survey_glm <- svyglm(form_loop, sub.y)
  anova_res <- aov(survey_glm)
  p_list[[i]] <- summary(anova_res)[[1]]$'Pr(>F)'[[1]]

}
print(variable_in_loop)
print(median(unlist(p_list)))
}


# COMPARING CATEGORICAL PER CLUSTER

varlist <- c("gender", "marital",
"education", "ethnicity",
"ann_household_income",
"family_pov_level",
"asthma", "arthritis",
"heart_failure", "CHD",
"angina", "heart_attack",
"stroke", "bronchitis",
"liver_condition", "thyroid_condition",
"cancer",
"diabetes", "hypertension",
"hyperlipidemia" ,
"smoking", "alcohol",
"dep_category")

for (variable_in_loop in varlist) {
  # P value collection
  p_list <- list()
for (i in 1:15) {
  filename <- paste0("cluster_merged_",i,".rds")
  df <- readRDS(filename)

  # survey design
  nhanes.y <- svydesign(data=df, id=~SDMVPSU, strata=~SDMVSTRA, weights=~survey_weight, nest=TRUE)

  # subsetting data
  sub.y <- subset(nhanes.y, (df$depressed == 1))

  # chi squared
  pasted_formula <- paste0("~",variable_in_loop, " + cluster_membership")
  form_loop <- as.formula(pasted_formula)
  tbl <- svychisq(form_loop, sub.y)
  p_list[[i]] <- as.numeric(tbl$p.value)

}
  print(variable_in_loop)
  print(median(unlist(p_list)))
}