This document contains the meta-analysis of the surivival models, using time since baseline as the metric of interest and neuroticism by conscientiousness as the predictor of interest.

Code

The following packages were used to generate this table:

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)

meta.data.time <- data.frame()

n <- 0

for(i in study.names){
  n <- n+1
  x <- get(paste0(i, "_survival_output"))
  meta.data.time[n, "study"] <- i
  meta.data.time[n, "n_coef"] <- x$time$model1$coef["z.neur", "coef"]
  meta.data.time[n, "n_se"] <- x$time$model1$coef["z.neur", "se(coef)"]
  meta.data.time[n, "c_coef"] <- x$time$model1$coef["z.con", "coef"]
  meta.data.time[n, "c_se"] <- x$time$model1$coef["z.con", "se(coef)"]
  meta.data.time[n, "n"] <- x$time$model1$ntotal
  meta.data.time[n, "n_died"] <- x$descriptives$died.tab["1"]
}

meta.results.time.neur <- rma(yi = n_coef, 
                              sei = n_se, 
                              ni = n,  
                              measure = "RR",
                              slab = study, 
                              data = meta.data.time)

meta.results.time.con <- rma(yi = c_coef, 
                              sei = c_se, 
                              ni = n,  
                              measure = "RR",
                              slab = study, 
                              data = meta.data.time)

meta.data.time = meta.data.time %>%
  gather("key", "value", n_coef, n_se, c_coef, c_se) %>%
  separate("key", into = c("trait", "key")) %>%
  spread("key", "value")

meta.results.time = rma(yi = coef, 
                        sei = se, 
                        ni = n,  
                        measure = "RR",
                        slab = study, 
                        data = meta.data.time)

meta.data.time = meta.data.time %>%
  #mutate(study = gsub("LBC", "LBC1936", study)) %>%
  arrange(trait, study)

#find plot limits
max.ci = meta.data.time %>%
  mutate(ci = exp(coef+1.96*se)) %>%
  arrange(desc(ci))
max.ci = max.ci[2, "ci"]

#max.ci = max(exp(neur.data$estimate+1.96*neur.data$se))
min.ci = min(exp(meta.data.time$coef-1.96*meta.data.time$se))

range = max.ci-min.ci

lower = min.ci-(range)
upper = max.ci+(range)*.5

#estimate position of extra information
pos = min.ci-lower
pos = pos/2
pos = c(lower+.8*pos, lower+1.6*pos)

unique = nrow(meta.data.time)/2
rows = c(1:unique, (unique+6):((2*unique)+5))
cex.set = .65

forest(meta.data.time$coef, meta.data.time$se^2,
       cex = cex.set,
       slab = meta.data.time$study,
       xlim = c(lower, upper),
       alim = c(min.ci, max.ci),
       transf=exp,
       rows = rows[order(rows,decreasing = T)],
       ylim = c(-1, 5+max(rows)),
       refline = 1,
       ilab = meta.data.time[,c("n", "n_died")],
       ilab.xpos = pos)

addpoly(meta.results.time.con, row = unique+5, 
        cex = cex.set,transf =exp, mlab="", col = "blue")
addpoly(meta.results.time.neur, row = min(rows)-1, 
        cex = cex.set,transf =exp, mlab="", col = "blue")

text(lower, unique+5, pos=4, cex = cex.set,
     bquote(paste("(Q = ",
                  .(formatC(meta.results.time.con$QE, digits=2, format="f")),
                  ", df = ", .(meta.results.time.con$k - meta.results.time.con$p),
                  ", p = ", .(formatC(meta.results.time.con$QEp, digits=2, format="f")), 
                  "; ", I^2, " = ",
                  .(formatC(meta.results.time.con$I2, digits=1, format="f")), "%)")))
text(lower, min(rows)-1, pos=4, cex = cex.set,
     bquote(paste("(Q = ",
                  .(formatC(meta.results.time.neur$QE, digits=2, format="f")),
                  ", df = ", .(meta.results.time.neur$k - meta.results.time.neur$p),
                  ", p = ", .(formatC(meta.results.time.neur$QEp, digits=2, format="f")), 
                  "; ", I^2, " = ",
                  .(formatC(meta.results.time.neur$I2, digits=1, format="f")), "%)")))
text(lower, 5.5+max(rows),
     "Study", cex = cex.set*1.2, pos = 4)
text(upper, 5.5+max(rows),
     "Risk Ratio [95% CI]", cex = cex.set*1.2, pos=2)
text(lower, max(rows)+1.5,
     "Main Effect of Conscientiousness", cex = cex.set*1.1, pos = 4, font = 2)
text(lower, unique+2,
     "Main Effect of Neuroticism", cex = cex.set*1.1, pos = 4, font = 2)

Meta analysis of main effects (time since baseline as metric)

Code