survival_biomarkers_subgroups: Tabulate biomarker effects on survival by subgroup

In tern: Create Common TLGs Used in Clinical Trials

Tabulate biomarker effects on survival by subgroup

Description

Tabulate the estimated effects of multiple continuous biomarker variables across population subgroups.

Usage

tabulate_survival_biomarkers(
  df,
  vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
  groups_lists = list(),
  control = control_coxreg(),
  label_all = lifecycle::deprecated(),
  time_unit = NULL,
  na_str = default_na_str(),
  .indent_mods = 0L
)

Arguments

df	(data.frame) containing all analysis variables, as returned by extract_survival_biomarkers().
vars	(character) the names of statistics to be reported among: n_tot_events: Total number of events per group. n_tot: Total number of observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval of hazard ratio. pval: p-value of the effect. Note, one of the statistics n_tot and n_tot_events, as well as both hr 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.
control	(list) a list of parameters as returned by the helper function control_coxreg().
label_all	please assign the label_all parameter within the extract_survival_biomarkers() function when creating df.
time_unit	(string) label with unit of median survival time. Default NULL skips displaying unit.
na_str	(string) string used to replace all NA or empty values in the output.
.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.

Value

An rtables table summarizing biomarker effects on survival by subgroup.

Functions

• tabulate_survival_biomarkers(): Table-creating function which creates a table summarizing biomarker effects on survival by subgroup.

Note

In contrast to tabulate_survival_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_surv_one_biomarker() which is used internally, extract_survival_biomarkers().

Examples

library(dplyr)

adtte <- tern_ex_adtte

# Save variable labels before data processing steps.
adtte_labels <- formatters::var_labels(adtte)

adtte_f <- adtte %>%
  filter(PARAMCD == "OS") %>%
  mutate(
    AVALU = as.character(AVALU),
    is_event = CNSR == 0
  )
labels <- c("AVALU" = adtte_labels[["AVALU"]], "is_event" = "Event Flag")
formatters::var_labels(adtte_f)[names(labels)] <- labels

# Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`,
# in multiple regression models containing one covariate `RACE`,
# as well as one stratification variable `STRATA1`. The subgroups
# are defined by the levels of `BMRKR2`.

df <- extract_survival_biomarkers(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    biomarkers = c("BMRKR1", "AGE"),
    strata = "STRATA1",
    covariates = "SEX",
    subgroups = "BMRKR2"
  ),
  label_all = "Total Patients",
  data = adtte_f
)
df

# Here we group the levels of `BMRKR2` manually.
df_grouped <- extract_survival_biomarkers(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    biomarkers = c("BMRKR1", "AGE"),
    strata = "STRATA1",
    covariates = "SEX",
    subgroups = "BMRKR2"
  ),
  data = adtte_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.
tabulate_survival_biomarkers(df)

## Table with a manually chosen set of columns: leave out "pval", reorder.
tab <- tabulate_survival_biomarkers(
  df = df,
  vars = c("n_tot_events", "ci", "n_tot", "median", "hr"),
  time_unit = as.character(adtte_f$AVALU[1])
)

## Finally produce the forest plot.

g_forest(tab, xlim = c(0.8, 1.2))

tern documentation built on June 22, 2024, 10:25 a.m.