Code

The following packages were used to generate this table:

library(papaja)
library(tidyverse)
library(knitr)
library(kableExtra)
library(here)

The files needed for this table are available at osf.io/mzfu9 in the Individual Study Output folder.

First we load the individual study analysis objects.

study.names = c("EAS", "HRS", "LBC", "LBLS", "MAP", "MAS", "MIDUS", "NAS", "OATS", "ROS", "SLS", "WLS")
lapply(here(paste0("mortality/study output/", study.names, "_survival_output.Rdata")), load, .GlobalEnv)

Next we extract the relevant statistics for each study.

survival.df = data.frame(study = study.names)

survival.df$full = lapply(X = paste0(study.names, "_survival_output"), 
                          FUN = function(x) get(x)$descriptives$survival_full)
survival.df$died = lapply(X = paste0(study.names, "_survival_output"), 
                          FUN = function(x) get(x)$descriptives$survival_died)

survival.df = survival.df %>%
  gather(key = "sample", value = "value", -study) %>%
  unnest() %>%
  group_by(study) %>%
  mutate(percent = case_when(sample == "died" ~ n/max(n)*100)) %>%
  dplyr::select(study, sample, n, percent, mean, sd, min, max) %>%
  arrange(study)  %>%
  ungroup()

We identify the rows for each variable.

rows.EAS = which(survival.df$study == "EAS"); rows.EAS = c(min(rows.EAS), max(rows.EAS))
rows.HRS = which(survival.df$study == "HRS"); rows.HRS = c(min(rows.HRS), max(rows.HRS))
rows.LBC = which(survival.df$study == "LBC"); rows.LBC = c(min(rows.LBC), max(rows.LBC))
rows.LBLS = which(survival.df$study == "LBLS"); rows.LBLS = c(min(rows.LBLS), max(rows.LBLS))
rows.MAP = which(survival.df$study == "MAP"); rows.MAP = c(min(rows.MAP), max(rows.MAP))
rows.MAS = which(survival.df$study == "MAS"); rows.MAS = c(min(rows.MAS), max(rows.MAS))
rows.MIDUS = which(survival.df$study == "MIDUS"); rows.MIDUS = c(min(rows.MIDUS), max(rows.MIDUS))
rows.NAS = which(survival.df$study == "NAS"); rows.NAS = c(min(rows.NAS), max(rows.NAS))
rows.OATS = which(survival.df$study == "OATS"); rows.OATS = c(min(rows.OATS), max(rows.OATS))
rows.ROS = which(survival.df$study == "ROS"); rows.ROS = c(min(rows.ROS), max(rows.ROS))
rows.SLS = which(survival.df$study == "SLS"); rows.SLS = c(min(rows.SLS), max(rows.SLS))
rows.WLS = which(survival.df$study == "WLS"); rows.WLS = c(min(rows.WLS), max(rows.WLS))

survival.df %>%
  dplyr::select(-study) %>%
  kable(., booktabs = TRUE, escape = F, digits = 2, format = "html", 
        col.names = c("Sample", "N", "Percent of full sample",
                      "Mean", "SD", "Min", "Max")) %>%
  kable_styling(full_width = T, latex_options = c("repeat_header")) %>%
  add_header_above(c(" "=3, "Surival Time (in months)" = 4)) %>%
  group_rows("EAS", rows.EAS[1], rows.EAS[2]) %>%
  group_rows("HRS", rows.HRS[1], rows.HRS[2]) %>%
  group_rows("LBC1936", rows.LBC[1], rows.LBC[2]) %>%
  group_rows("LBLS", rows.LBLS[1], rows.LBLS[2]) %>%
  group_rows("MAP", rows.MAP[1], rows.MAP[2]) %>%
  group_rows("MAS", rows.MAS[1], rows.MAS[2]) %>%
  group_rows("MIDUS", rows.MIDUS[1], rows.MIDUS[2]) %>%
  group_rows("NAS", rows.NAS[1], rows.NAS[2]) %>%
  group_rows("OATS", rows.OATS[1], rows.OATS[2]) %>%
  group_rows("ROS", rows.ROS[1], rows.ROS[2]) %>%
  group_rows("SLS", rows.SLS[1], rows.SLS[2]) %>%
  group_rows("WLS", rows.WLS[1], rows.WLS[2])