In bashlee/test: Provides a suite of extension functions for NMA using the `gemtc` package

knitr::opts_chunk$set(echo = FALSE)


library(dplyr)
library(reshape2)
library(ggplot2)
library(gemtc)
library(pander)

General information

The NMA was run via the gemtc package.

Input settings

## read in results from Bayesian fit
files_all <- dir(system.file("extdata", "results", package = "gemtcPlus"))
files_rda <- grep("out-[0-9].RData", files_all, value = TRUE)
out_all <- vector(mode = "list", length = length(files_rda))

for(i in seq_along(files_rda)){

  out_i <- system.file("extdata",
                   "results",
                   files_rda[i],
                   package = "gemtcPlus")
  load(out_i)

  out_all[[i]] <- out
  rm(out)
}

Input data

Table Input data

dhr <- out_all[[1]]$data.raw
#pandoc.table(dhr, row.names = FALSE, split.tables = Inf, justify = rep("right", ncol(dhr)), digits = 3)

Figure Network

plot(out_all[[1]]$model)

Model comparison

Table Model fit statistics

dsum <- data.frame(Name = unlist(lapply(out_all, FUN = function(x) x$name)),
                   do.call(rbind, lapply(out_all, get_mtc_sum))
)
pandoc.table(dsum, row.names = FALSE)

\newline \newline

Table Model descriptions

descr <- data.frame(Name = unlist(lapply(out_all, FUN = function(x) x$name)),
                    Type = unlist(lapply(out_all, FUN = function(x) x$model$linearModel)),
                    RE.prior = unlist(lapply(out_all, FUN = function(x) paste(x$model$hy.prior)))
)
pandoc.table(descr, row.names = FALSE)

NMA results

HR estimates

Comparison vs ref treatment "A".

for(i in seq_along(out_all)){
  out_i   <- out_all[[i]]
  title_i <- paste("Model:", out_i$name)

  HR_i    <- get_mtc_newVsAll(out_i, new.lab = "A", transform = "exp", digits = 2)

  pBetter_i <- get_mtc_probBetter(x = out_i, new.lab = "A", smaller.is.better = TRUE, sort.by = "effect")

  HR_i <- HR_i %>% left_join(pBetter_i[, c("Comparator", "probNewBetter")], by = "Comparator")

  cat("###", title_i, "\n")
  cat("__Table__ Hazard ratios ", attr(HR_i, "comparison"), "and probability of new treatment being better\n")
  pandoc.table(HR_i[order(HR_i$Med),], row.names = FALSE)
  cat("\\linebreak\n\n")
  cat("__Figure__ Hazard ratios ", attr(HR_i, "comparison"), "\n")
  plot_mtc_forest(x = HR_i, lab = paste("Hazard ratio", attr(HR_i, "comparison")), sort.by = "effect")
  cat("\n\n")
}

Cross-tabulation of HRs

for(i in seq_along(out_all)){
  out_i   <- out_all[[i]]
  title_i <- paste("Model:", out_i$name)
  ctab_i  <- round(exp(relative.effect.table(out_i)), 2)


  cat("###", title_i, "\n")
  cat("__Table__ Cross-table of hazard ratios (column to row)\n")
  pandoc.table(as.data.frame(ctab_i), split.tables = Inf)
  cat("\n\n")
}

Model code (jags)

for(i in seq_along(out_all)){
  out_i   <- out_all[[i]]
  title_i <- paste("Model:", out_i$name)

  cat("###", title_i, "\n")
  cat("```\n")
  cat(out_i$model$code)
  cat("\n")
  cat("```\n\n")
}