In Merck/metalite.ae: Adverse Events Analysis Using 'metalite'

knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  out.width = "100%",
  dpi = 150
)

library(metalite.ae)

Overview

The AE specific subgroup analysis aims to provide tables to summarize details of adverse events by subgroup. The development of AE specific subgroup analysis involves exported functions:

• prepare_ae_specific_subgroup(): prepare analysis raw datasets.
• format_ae_specific_subgroup(): prepare analysis (mock) outdata with proper format.
• tlf_ae_specific_subgroup(): transfer (mock) output dataset to RTF table.

Analysis preparation

The prepare_ae_specific_subgroup() function is designed to be used for multiple purposes. The input of the function is a meta object created by the metalite package.

meta <- meta_ae_example()

The output of the function is an outdata object containing a list of analysis raw datasets. Key arguments are subgroup_var, subgroup_header, and display_subgroup_total.

outdata <- prepare_ae_specific_subgroup(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel",
  subgroup_var = "SEX",
  subgroup_header = c("TRTA", "SEX"),
  display_subgroup_total = TRUE
)

outdata

The output dataset contains commonly used statistics within each subgroup_var.

outdata$out_all$F
outdata$out_all$M
outdata$out_all$Total

The variable is indexed by the order of outdata$group and outdata$subgroup within each subgroup_var.

outdata$group
outdata$subgroup

The row is indexed by the order of name within each subgroup_var analysis output.

head(data.frame(outdata$out_all$Total$order, outdata$out_all$Total$name))

• n_pop: participants in population within each subgroup_var.

outdata$out_all$F$n_pop
outdata$out_all$M$n_pop
outdata$out_all$Total$n_pop

• n: number of subjects with AE within each subgroup_var.

head(outdata$out_all$F$n)
head(outdata$out_all$M$n)
head(outdata$out_all$Total$n)

• prop: proportion of subjects with AE within each subgroup_var.

head(outdata$out_all$F$prop)
head(outdata$out_all$M$prop)
head(outdata$out_all$Total$prop)

• diff: risk difference compared with the reference_group within each subgroup_var.

head(outdata$out_all$Total$diff)

Format output

After we have the raw analysis results, we can use format_ae_specific_subgroup() to prepare the outdata to create RTF tables.

tbl <- outdata |> format_ae_specific_subgroup()
head(tbl$tbl)

We can hide the total column:

tbl <- outdata |> format_ae_specific_subgroup(display = c("n", "prop"))
head(tbl$tbl)

Adding risk difference:

tbl <- outdata |> format_ae_specific_subgroup(display = c("n", "prop", "diff"))
head(tbl$tbl)

Mock data preparation

We can also use format_ae_specific_subgroup() to create mock output data.

The purpose of the mock argument is not to create a comprehensive mock table template, but a handy way to help users create a mock table that mimics the exact output layout.

Additional work is required to develop a flexible mock table generation tool (for example, a dedicated mock table generation package).

tbl <- outdata |> format_ae_specific_subgroup(mock = FALSE)
head(tbl$tbl)

RTF tables

By using tlf_ae_specific_subgroup(), we can transfer the output from format_ae_specific_subgroup() to an RTF or PDF table.

outdata |>
  format_ae_specific_subgroup() |>
  tlf_ae_specific_subgroup(
    meddra_version = "24.0",
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    analysis = "ae_specific", # Provide analysis type defined in meta$analysis
    path_outtable = "rtf/ae0specific0sub0gender1.rtf"
  )

knitr::include_graphics("pdf/ae0specific0sub0gender1.pdf")

Merck/metalite.ae documentation built on Feb. 10, 2025, 5:03 p.m.