In CJangelo/SPR: Tools to Learn Statistical Programming in R

Data Generation

1. Generate longitudinal binomial data
2. Marginal model - ordinal GEE should work.

Simulation Study

First step is to recover estimates with full data

1. use GEE
2. use GLMM and then convert back to population average estimate
3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5650580/#R11
4. https://projecteuclid.org/download/pdf_1/euclid.ss/1009212671

Next is to figure out a way to compute the standard errors of the population average. Unclear how to do that unless you bootstrap.

After that, test using missing data.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

rm(list = ls())
gc()

library(MASS)
library(SPR)
library(geepack)
library(lme4)

# Initialize outputs: out1 is for the means/medians
est.names <- c('GLM', 'GEE', 'GLMM', 'GLMM_pop_avg_logit','GLMM_pop_avg_probit')
number.param <- 8 # see Beta
out <- replicate(n = length(est.names),
          expr = as.data.frame(matrix(ncol=number.param,nrow=0)),
          simplify = F)
  names(out) <- est.names

  number.repl <- 100

for (repl in 1:number.repl) {


  set.seed(5282021 + repl)

 sim.out <- SPR::sim_dat_binom(N = 1000,
                               number.groups = 2 ,
                               number.timepoints = 4,
                               reg.formula = formula( ~ Time + Group + Time*Group),
                               Beta = 1,
                               corr = 'ar1',
                               cor.value = 0.4)


  dat <- sim.out$dat

# GLM
  mod.glm1 <- stats::glm(Y_comp ~ Time + Group + Time*Group,
                         family = 'binomial',
                         data = dat)

  # Generalized Estimating Equations:
  mod.gee1 <- geepack::geeglm(Y_comp ~ Time + Group + Time*Group,
                              id = id.geepack,
                              data = dat,
                              family = binomial, corstr = "ar1")


  # GLMM
  mod.glmm <- lme4::glmer(Y_comp ~ Time + Group + Time*Group + (1|USUBJID),
                          family = 'binomial',
                          data = dat)

  sigma2 <- lme4::VarCorr(mod.glmm)
  sigma2 <- as.data.frame(sigma2)$vcov
  denom.logit <- sqrt( (sigma2 + (pi^2/3))/(pi^2/3) )
  denom.probit <- sqrt( 1 + sigma2)

      #rbind all this shit
      out[['GLM']][repl, ] <- coef(mod.glm1)
              #rbind.data.frame(out[['GLM']], coef(mod.glm1))
      out[['GEE']][repl, ] <- coef(mod.gee1)
        #rbind(out[['GEE']], coef(mod.gee1))
      out[['GLMM']][repl, ] <- fixef(mod.glmm)
        #rbind(out[['GLMM']], fixef(mod.glmm))
      out[['GLMM_pop_avg_logit']][repl, ] <- fixef(mod.glmm)/denom.logit
        #rbind(out[['GLMM_pop_avg_logit']], as.vector(fixef(mod.glmm)/denom.logit))
      out[['GLMM_pop_avg_probit']][repl, ] <- fixef(mod.glmm)/denom.probit
        #rbind(out[['GLMM_pop_avg_probit']], fixef(mod.glmm)/denom.probit)

  cat(paste0('Replication: ', repl, '\n'))

}# end replications



  out2 <- lapply(out, function(x) setNames(object = x, nm = rownames(sim.out$Beta)))
  tmp <- lapply(out2, colMeans)
  out3 <- cbind(sim.out$Beta, rbind.data.frame(tmp))

CJangelo/SPR documentation built on Feb. 24, 2022, 5:02 a.m.