tern (version 0.9.7)

h_survival_biomarkers_subgroups: 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,
  ...
)

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.

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.

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"
)