tern (version 0.9.8)

response_subgroups: Tabulate binary response by subgroup

Description

The tabulate_rsp_subgroups() function creates a layout element to tabulate binary response by subgroup, 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_subgroups(), 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_subgroups(
  lyt,
  df,
  vars = c("n_tot", "n", "prop", "or", "ci"),
  groups_lists = list(),
  label_all = lifecycle::deprecated(),
  riskdiff = NULL,
  na_str = default_na_str(),
  ...,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)
a_response_subgroups(
  df,
  labelstr = "",
  ...,
  .stats = NULL,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Value

An rtables table summarizing binary response by subgroup.

• a_response_subgroups() returns the corresponding list with formatted rtables::CellValue().

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

df

(list)
a list of data frames containing all analysis variables. List should be created using extract_rsp_subgroups().

vars

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

• n: Total number of observations per group.

• n_rsp: Number of responders per group.

• prop: Proportion of responders.

• n_tot: Total number of observations.

• 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.

groups_lists

(named list of list)
optionally contains for each subgroups variable a list, which specifies the new group levels via the names and the levels that belong to it in the character vectors that are elements of the list.

label_all

(string)
label for the total population analysis.

riskdiff

(list)
if a risk (proportion) difference column should be added, a list of settings to apply within the column. See control_riskdiff() for details. If NULL, no risk difference column will be added. If riskdiff$arm_x and riskdiff$arm_y are NULL, the first level of df$prop$arm will be used as arm_x and the second level as arm_y.

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.

labelstr

(string)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information.

.stats

(character)
statistics to select for the table.

Functions

• tabulate_rsp_subgroups(): Table-creating function which creates a table summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() and rtables::summarize_row_groups().

• a_response_subgroups(): Formatted analysis function which is used as afun in tabulate_rsp_subgroups().

Details

These functions create a layout starting from a data frame which contains the required statistics. Tables typically used as part of forest plot.

See Also

extract_rsp_subgroups()

Examples

library(dplyr)
library(forcats)

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

adrs_f <- adrs %>%
  filter(PARAMCD == "BESRSPI") %>%
  filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
  droplevels() %>%
  mutate(
    # Reorder levels of factor to make the placebo group the reference arm.
    ARM = fct_relevel(ARM, "B: Placebo"),
    rsp = AVALC == "CR"
  )
formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")

# Unstratified analysis.
df <- extract_rsp_subgroups(
  variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
  data = adrs_f
)
df

# Stratified analysis.
df_strat <- extract_rsp_subgroups(
  variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2"), strata = "STRATA1"),
  data = adrs_f
)
df_strat

# Grouping of the BMRKR2 levels.
df_grouped <- extract_rsp_subgroups(
  variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
  data = adrs_f,
  groups_lists = list(
    BMRKR2 = list(
      "low" = "LOW",
      "low/medium" = c("LOW", "MEDIUM"),
      "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
    )
  )
)
df_grouped

# Table with default columns
basic_table() %>%
  tabulate_rsp_subgroups(df)

# Table with selected columns
basic_table() %>%
  tabulate_rsp_subgroups(
    df = df,
    vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci")
  )

# Table with risk difference column added
basic_table() %>%
  tabulate_rsp_subgroups(
    df,
    riskdiff = control_riskdiff(
      arm_x = levels(df$prop$arm)[1],
      arm_y = levels(df$prop$arm)[2]
    )
  )