FSTG01 Subgroup Analysis of Best Overall Response.

Usage

fstg01_main(
  adam_db,
  dataset = "adrs",
  arm_var = "ARM",
  rsp_var = "IS_RSP",
  subgroups = c("SEX", "AGEGR1", "RACE"),
  strata_var = NULL,
  stat_var = c("n_tot", "n", "n_rsp", "prop", "or", "ci"),
  max_colwidth = 12,
  ...
)

fstg01_pre(adam_db, ...)

fstg01

Format

An object of class chevron_g of length 1.

Arguments

adam_db

(list of data.frames) object containing the ADaM datasets

dataset

(string) the name of a table in the adam_db object.

arm_var

(string) the arm variable name used for group splitting.

rsp_var

(string) the response variable name to flag whether each subject is a binary response or not.

subgroups

(character) the subgroups variable name to list baseline risk factors.

strata_var

(character) required if stratified analysis is performed.

stat_var

(character) the names of statistics to be reported in tabulate_rsp_subgroups.

max_colwidth

(int) maximum width of columns. Stratification label longer than this will be truncated.

...

Further arguments passed to g_forest and extract_rsp_subgroups (a wrapper for h_odds_ratio_subgroups_df and h_proportion_subgroups_df). For details, see the documentation in tern. Commonly used arguments include col_symbol_size, col, vline, groups_lists, conf_level, method, label_all, etc.

Value

a gTree object.

Details

• No overall value.

• Keep zero count rows by default.

Functions

• fstg01_main(): Main TLG Function

• fstg01_pre(): Preprocessing

Note

• adam_db object must contain the table specified by dataset with "PARAMCD", "ARM", "AVALC", and the columns specified by subgroups which is denoted as c("SEX", "AGEGR1", "RACE") by default.

• If the plot is too large to be rendered in the output, please provide width_row_names, width_columns and width_forest manually to make it fit. See g_forest for more details.

Examples

library(dplyr)
library(dunlin)

proc_data <- log_filter(
  syn_data,
  PARAMCD == "BESRSPI" & ARM %in% c("A: Drug X", "B: Placebo"), "adrs"
)
run(fstg01, proc_data,
  subgroups = c("SEX", "AGEGR1", "RACE"),
  conf_level = 0.90, dataset = "adrs"
)
#> gTree[GRID.gTree.11]