Helper functions for tabulation of a single biomarker result

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,
  ...
)

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.

Value

An rtables table object with statistics in columns.

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")
)
#> Warning: `h_tab_rsp_one_biomarker()` was deprecated in tern 0.9.8.
#> ℹ This function is no longer used within `tern`.
#> Warning: `h_tab_one_biomarker()` was deprecated in tern 0.9.8.
#> ℹ This function is no longer used within `tern`.
#> ℹ The deprecated feature was likely used in the tern package.
#>   Please report the issue at
#>   <https://github.com/insightsengineering/tern/issues>.
#>                Total n   Responders   Response (%)   Odds Ratio      95% CI      p-value (Wald)
#> ———————————————————————————————————————————————————————————————————————————————————————————————
#> All patients     200        164          82.0%          0.98      (0.88, 1.08)       0.6353    

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"
)
#> Warning: `h_tab_surv_one_biomarker()` was deprecated in tern 0.9.8.
#> ℹ This function is no longer used within `tern`.
#>                Total n   Total Events   Median (days)   Hazard Ratio   95% Wald CI    p-value (Wald)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————
#> All patients     200         141            753.5           1.00       (0.95, 1.05)       0.9941