response_biomarkers_subgroups: Tabulate biomarker effects on binary response by subgroup

Description

The tabulate_rsp_biomarkers() function creates a layout element to tabulate the estimated biomarker effects on a binary response endpoint across subgroups, returning statistics including response rate and odds ratio for each population subgroup. The table is created from df, a list of data frames returned by extract_rsp_biomarkers(), with the statistics to include specified via the vars parameter.

A forest plot can be created from the resulting table using the g_forest() function.

Usage

tabulate_rsp_biomarkers(
  df,
  vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval"),
  na_str = default_na_str(),
  ...,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Value

An rtables table summarizing biomarker effects on binary response by subgroup.

Arguments

df

(data.frame)
containing all analysis variables, as returned by extract_rsp_biomarkers().

vars

(character)
the names of statistics to be reported among:

n_tot: Total number of patients per group.
n_rsp: Total number of responses per group.
prop: Total response proportion per group.
or: Odds ratio.
ci: Confidence interval of odds ratio.
pval: p-value of the effect. Note, the statistics n_tot, or and ci are required.

na_str

(string)
string used to replace all NA or empty values in the output.

...

additional arguments for the lower level functions.

.stat_names

(character)
names of the statistics that are passed directly to name single statistics (.stats). This option is visible when producing rtables::as_result_df() with make_ard = TRUE.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

Details

These functions create a layout starting from a data frame which contains the required statistics. The tables are then typically used as input for forest plots.

Examples

Run this code

library(dplyr)
library(forcats)

adrs <- tern_ex_adrs
adrs_labels <- formatters::var_labels(adrs)

adrs_f <- adrs %>%
  filter(PARAMCD == "BESRSPI") %>%
  mutate(rsp = AVALC == "CR")
formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")

df <- extract_rsp_biomarkers(
  variables = list(
    rsp = "rsp",
    biomarkers = c("BMRKR1", "AGE"),
    covariates = "SEX",
    subgroups = "BMRKR2"
  ),
  data = adrs_f
)

# \donttest{
## Table with default columns.
tabulate_rsp_biomarkers(df)

## Table with a manually chosen set of columns: leave out "pval", reorder.
tab <- tabulate_rsp_biomarkers(
  df = df,
  vars = c("n_rsp", "ci", "n_tot", "prop", "or")
)

## Finally produce the forest plot.
g_forest(tab, xlim = c(0.7, 1.4))
# }