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Create Data Generating Mechanism for MRCT Simulations

Source: R/mrct_simulation.R
create_dgm_for_mrct.Rd

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
)

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

Value

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

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.

See also

generate_aft_dgm_flex for underlying DGM creation mrct_region_sims for running simulations with the DGM

Examples

if (FALSE) { # \dontrun{
# Prepare data
df_case <- read.csv("data/dfsynthetic.csv")
df_case$regA <- df_case$region_asia

# Alternative hypothesis (heterogeneous treatment effect)
dgm_alt <- create_dgm_for_mrct(
  df_case = df_case,
  model_type = "alt",
  log_hrs = log(c(3, 1.25, 0.50)),
  verbose = TRUE
)

# Null hypothesis (uniform effect)
dgm_null <- create_dgm_for_mrct(
  df_case = df_case,
  model_type = "null",
  verbose = TRUE
)

# With forced confounder effect
dgm_conf <- create_dgm_for_mrct(
  df_case = df_case,
  model_type = "alt",
  confounder_var = "prior_treat",
  confounder_effect = log(1.5),
  verbose = TRUE
)
} # }