h_survival_biomarkers_subgroups {tern}

Helper functions for tabulating biomarker effects on survival by subgroup

Description

Helper functions which are documented here separately to not confuse the user when reading about the user-facing functions.

Usage

h_surv_to_coxreg_variables(variables, biomarker)

h_coxreg_mult_cont_df(variables, data, control = control_coxreg())

h_tab_surv_one_biomarker(
  df,
  vars,
  time_unit,
  na_str = default_na_str(),
  .indent_mods = 0L,
  ...
)

Arguments

variables

(named list of string)
list of additional analysis variables.

biomarker

(string)
the name of the biomarker variable.

data

(data.frame)
the dataset containing the variables to summarize.

control

(list)
a list of parameters as returned by the helper function control_coxreg().

df

(data.frame)
results for a single biomarker, as part of what is returned by extract_survival_biomarkers() (it needs a couple of columns which are added by that high-level function relative to what is returned by h_coxreg_mult_cont_df(), see the example).

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.

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.

...

additional arguments for the lower level functions.

Value

• h_surv_to_coxreg_variables() returns a named list of elements time, event, arm, covariates, and strata.

• h_coxreg_mult_cont_df() returns a data.frame containing estimates and statistics for the selected biomarkers.

• h_tab_surv_one_biomarker() returns an rtables table object with the given statistics arranged in columns.

Functions

• h_surv_to_coxreg_variables(): Helps with converting the "survival" function variable list to the "Cox regression" variable list. The reason is that currently there is an inconsistency between the variable names accepted by extract_survival_subgroups() and fit_coxreg_multivar().

• h_coxreg_mult_cont_df(): Prepares estimates for number of events, patients and median survival times, as well as hazard ratio estimates, confidence intervals and p-values, for multiple biomarkers in a given single data set. variables corresponds to names of variables found in data, passed as a named list and requires elements tte, is_event, biomarkers (vector of continuous biomarker variables) and optionally subgroups and strata.

• h_tab_surv_one_biomarker(): Prepares a single sub-table given a df_sub containing the results for a single biomarker.

Examples

library(dplyr)
library(forcats)

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

# This is how the variable list is converted internally.
h_surv_to_coxreg_variables(
  variables = list(
    tte = "AVAL",
    is_event = "EVNT",
    covariates = c("A", "B"),
    strata = "D"
  ),
  biomarker = "AGE"
)

# For a single population, estimate separately 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
)
df

# If the data set is empty, still the corresponding rows with missings are returned.
h_coxreg_mult_cont_df(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    biomarkers = c("BMRKR1", "AGE"),
    covariates = "REGION1",
    strata = c("STRATA1", "STRATA2")
  ),
  data = adtte_f[NULL, ]
)

# 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"
)