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Prepare Censoring Model Parameters

Source: R/generate_aft_dgm_helpers.R
prepare_censoring_model.Rd

Constructs the censoring model object and appends per-subject counterfactual censoring linear predictors (lin_pred_cens_0, lin_pred_cens_1) to the super-population data frame.

Usage

prepare_censoring_model(
  df_work,
  cens_type,
  cens_params,
  df_super,
  select_censoring = TRUE,
  verbose = TRUE
)

Arguments

df_work

Working data frame (output of prepare_working_dataset).

cens_type

Character. "weibull" or "uniform".

cens_params

Named list of user-supplied censoring parameters.

df_super

Super-population data frame; receives lin_pred_cens_0 and lin_pred_cens_1 columns.

select_censoring

Logical. If TRUE (default), fits the censoring distribution from observed data using AIC-based survreg model comparison. If FALSE, uses cens_params directly with no model fitting. See generate_aft_dgm_flex for the required cens_params structure under each combination of select_censoring and cens_type.

verbose

Logical. If TRUE (default), prints the censoring model comparison table and recommendation. Set to FALSE to suppress all censoring model selection output.

Value

A named list:

cens_model

List of censoring distribution parameters stored in dgm$model_params$censoring.

df_super

Updated super-population data frame with lin_pred_cens_0 and lin_pred_cens_1 appended. These hold covariate contributions only (\(\gamma_c' X\)); the intercept is excluded.

Details

Linear predictor convention

lin_pred_cens_0 and lin_pred_cens_1 store the covariate contribution only — i.e. \(\gamma_c' X\), with the intercept \(\mu_c\) excluded. This matches the convention used for the outcome model (lin_pred_0, lin_pred_1 = \(\gamma' X\), no intercept) computed in calculate_linear_predictors().

simulate_from_dgm() reconstructs the full log-censoring time as: $$\log C = \mu_c + \delta + \tau_c \epsilon + \gamma_c' X$$ where \(\mu_c\) = params$censoring$mu, \(\delta\) = cens_adjust, \(\tau_c\) = params$censoring$tau, and \(\gamma_c' X\) = lin_pred_cens_{0|1}.

When select_censoring = TRUE, predict(survreg, type = "linear") returns the full linear predictor \(\mu_c + \gamma_c' X\). The stored intercept \(\mu_c\) is therefore subtracted before writing lin_pred_cens_*, so that simulate_from_dgm() can add params$censoring$mu exactly once. Omitting this subtraction causes \(\mu_c\) to be counted twice, producing astronomically large censoring times and universal censoring.

When select_censoring = FALSE with a Weibull/lognormal cens_type, the intercept-only model has zero covariate contribution, so lin_pred_cens_0 = lin_pred_cens_1 = 0. Storing mu instead of 0 causes the same double-counting.