Cox regression with simple additive model for Nested Case-Control NCC study

Usage

wrapper_ncc_cox_regression_core_prognostic(
  data,
  tte_var,
  censor_var,
  covariate_vars,
  ncc_vars,
  samplestat_var,
  m,
  match.var,
  match.int,
  return_vars = NULL,
  variable_names = NULL,
  caption = NULL,
  print_pvalues = TRUE,
  print_adjpvalues = TRUE
)

Arguments

data: Data frame preprocessed for the NCC analysis with 'multipleNCC::wpl()'.
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.
covariate_vars: Vector with names of covariates that are included in the formula of the simple additive model.
ncc_vars: Vector of names of covariates that were measured in the NCC.
samplestat_var: Name of variable indicating samplestat values. See 'multipleNCC::wpl()'.
m: See 'multipleNCC::wpl()'.
match.var: See 'multipleNCC::wpl()'. It has to be a matrix of continuous values.
match.int: See 'multipleNCC::wpl()'.
return_vars: Vector with names of covariates for which the statistics should be returned. If NULL, statistics are returned for all covariates.
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.
print_pvalues: Logical. Whether to print p-values.

Details

If for a factor covariate that should be returned the reference level has zero count, results are set to NAs because this levels is not used as a reference which means that it is not possible to fit the model that we want.