create_dgm_for_mrct: Create Data Generating Mechanism for MRCT Simulations

Description

Wrapper function to create a data generating mechanism (DGM) for MRCT simulation scenarios using generate_aft_dgm_flex.

Usage

create_dgm_for_mrct(
  df_case,
  model_type = c("alt", "null"),
  log_hrs = NULL,
  confounder_var = NULL,
  confounder_effect = NULL,
  include_regA = TRUE,
  verbose = FALSE
)

Value

An object of class "aft_dgm_flex" for use with simulate_from_dgm and mrct_region_sims

Arguments

df_case: Data frame containing case study data
model_type: Character. Either "alt" (alternative hypothesis with heterogeneous treatment effects) or "null" (uniform treatment effect)
log_hrs: Numeric vector. Log hazard ratios for spline specification. If NULL, defaults are used based on model_type
confounder_var: Character. Name of a confounder variable to include with a forced prognostic effect. Default: NULL (no forced effect)
confounder_effect: Numeric. Log hazard ratio for confounder_var effect. Only used if confounder_var is specified
include_regA: Logical. Include regA as a factor in the model. Default: TRUE
verbose: Logical. Print detailed output. Default: FALSE

Details

Model Types

alt: Alternative hypothesis: Treatment effect varies by biomarker level (heterogeneous treatment effect). Default log_hrs create HR ranging from 2.0 (harm) to 0.5 (benefit) across biomarker range

null

Null hypothesis: Uniform treatment effect regardless of biomarker level. Default log_hrs = log(0.7) uniformly

Confounder Effects

By default, NO prognostic confounder effect is forced. The confounder_var and confounder_effect parameters allow optionally specifying ANY baseline covariate to have a fixed prognostic effect in the outcome model.

The regA variable (region indicator) is included as a factor by default but without a forced effect - its coefficient is estimated from data.