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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 name 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) and pval (p value of the effect). Note, the statistics n_tot, or and ci are required.

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))
}