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Tabulate Biomarker Effects on Binary Response by Subgroup

Source: R/response_biomarkers_subgroups.R
response_biomarkers_subgroups.Rd

[Stable]

Tabulate the estimated effects of multiple continuous biomarker variables on a binary response endpoint across population subgroups.

Usage 

tabulate_rsp_biomarkers(
  df,
  vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval")
)

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.

Value

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

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.

Note

In contrast to tabulate_rsp_subgroups() this tabulation function does not start from an input layout lyt. This is because internally the table is created by combining multiple subtables.

See also

h_tab_rsp_one_biomarker() which is used internally, extract_rsp_biomarkers().

Examples 

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")
if (FALSE) {
## Table with default columns.
# df <- <need_data_input_to_work>
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))
}