response_biomarkers_subgroups | R Documentation |
Tabulate the estimated effects of multiple continuous biomarker variables on a binary response endpoint across population subgroups.
tabulate_rsp_biomarkers(
df,
vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval"),
na_str = default_na_str(),
.indent_mods = 0L
)
df |
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vars |
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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.
An rtables
table summarizing biomarker effects on binary response by subgroup.
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.
h_tab_rsp_one_biomarker()
which is used internally, extract_rsp_biomarkers()
.
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
)
## 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))
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