Cox regression estimating treatment effect within biomarker subgroups — wrapper_cox_regression

For this analysis biomarkers must be categorical.

Usage

wrapper_cox_regression_treatment(
  data,
  tte_var,
  censor_var,
  treatment_var,
  biomarker_vars = NULL,
  adjustment_vars = NULL,
  strata_vars = NULL,
  strat2_var = NULL,
  variable_names = NULL,
  caption = NULL,
  sr_times = NULL,
  print_nevent = TRUE,
  print_mst = TRUE,
  print_total = TRUE,
  print_pvalues = TRUE,
  print_adjpvalues = TRUE,
  print_hr = TRUE,
  print_sr_cis = FALSE
)

Arguments

data: Data frame.
tte_var: Name of the time-to-event variable. This variable must be numeric.
censor_var: Name of the censor variable. It has to be numeric and encode 1 for event and 0 for censor.
treatment_var: Name of the variable with treatment arms.
biomarker_vars: Vector with names of categorical biomarkers. When NULL, overall treatment effect is estimated.
adjustment_vars: Vector of covariate names used for adjustment in the model.
strata_vars: Vector of covariates used as stratification factors in the model.
strat2_var: Name of the second stratification variable used for splitting the data.
variable_names: Named vector with variable names. If not supplied, variable names are created by replacing in column names underscores with spaces.
caption: Caption for the table with results.
sr_times: Vector of times used to compute survival rates, for example, percent of 3-year IDSF.
print_nevent: Logical. Whether to print numbers of events.
print_mst: Logical. Whether to print median survival time (MST).
print_total: Logical. Whether to print total number of samples and total number of events.
print_pvalues: Logical. Whether to print p-values.
print_adjpvalues: Logical. Whether to print adjusted p-values.