tern (version 0.9.8)

h_biomarkers_subgroups: Helper functions for tabulation of a single biomarker result

Description

Usage

h_tab_one_biomarker(
  df,
  afuns,
  colvars,
  na_str = default_na_str(),
  ...,
  .stats = NULL,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)
h_tab_rsp_one_biomarker(
  df,
  vars,
  na_str = default_na_str(),
  .indent_mods = 0L,
  ...
)
h_tab_surv_one_biomarker(
  df,
  vars,
  time_unit,
  na_str = default_na_str(),
  .indent_mods = 0L,
  ...
)

Value

An rtables table object with statistics in columns.

Arguments

df

(data.frame)
results for a single biomarker. For h_tab_rsp_one_biomarker(), the results returned by extract_rsp_biomarkers(). For h_tab_surv_one_biomarker(), the results returned by extract_survival_biomarkers().

afuns

(named list of function)
analysis functions.

colvars

(named list)
named list with elements vars (variables to tabulate) and labels (their labels).

na_str

(string)
string used to replace all NA or empty values in the output.

...

additional arguments for the lower level functions.

.stats

(character)
statistics to select for the table.

.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.

vars

(character)
variable names for the primary analysis variable to be iterated over.

time_unit

(string)
label with unit of median survival time. Default NULL skips displaying unit.

Functions

• h_tab_one_biomarker(): Helper function to calculate statistics in columns for one biomarker.

• h_tab_rsp_one_biomarker(): Helper function that prepares a single response sub-table given the results for a single biomarker.

• h_tab_surv_one_biomarker(): Helper function that prepares a single survival sub-table given the results for a single biomarker.

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")

# For a single population, separately estimate the effects of two biomarkers.
df <- h_logistic_mult_cont_df(
  variables = list(
    rsp = "rsp",
    biomarkers = c("BMRKR1", "AGE"),
    covariates = "SEX"
  ),
  data = adrs_f
)

# Starting from above `df`, zoom in on one biomarker and add required columns.
df1 <- df[1, ]
df1$subgroup <- "All patients"
df1$row_type <- "content"
df1$var <- "ALL"
df1$var_label <- "All patients"

h_tab_rsp_one_biomarker(
  df1,
  vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval")
)

adtte <- tern_ex_adtte

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

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

# For a single population, separately estimate the effects of two biomarkers.
df <- h_coxreg_mult_cont_df(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    biomarkers = c("BMRKR1", "AGE"),
    covariates = "SEX",
    strata = c("STRATA1", "STRATA2")
  ),
  data = adtte_f
)

# Starting from above `df`, zoom in on one biomarker and add required columns.
df1 <- df[1, ]
df1$subgroup <- "All patients"
df1$row_type <- "content"
df1$var <- "ALL"
df1$var_label <- "All patients"
h_tab_surv_one_biomarker(
  df1,
  vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
  time_unit = "days"
)