surv_weibull: Estimate Counterfactual Survival Functions Using Weibull Censoring Scores

Description

Estimate Counterfactual Survival Functions Using Weibull Censoring Scores

Usage

surv_weibull(
  data,
  time_var,
  event_var,
  treatment_var,
  eval_times = NULL,
  weight_result,
  censoring_formula,
  censoring_control = list(maxiter = 350)
)

Value

List containing:

survival_matrix: Matrix [time x J] of survival function estimates S^(j)(t).
eval_times: Time points evaluated.
treatment_levels: Treatment level values.
n_levels: Number of treatment levels.
weights_by_group: List of weight vectors by treatment group.
censoring_scores_by_group: List of censoring score vectors by group.
method, estimand: Weighting method and target estimand.
Etau: Normalization constant.
censoring_result, ps_result, weight_result: Input objects.
design_matrices: List with W (PS model) and X (censoring model).
Z_matrix: Binary indicator matrix [n x J] for treatment groups.
time_vec, event_vec: Original time and event vectors.

Arguments

data: Data frame.
time_var: Name of time variable.
event_var: Name of event indicator (1 = event, 0 = censored).
treatment_var: Name of treatment variable.
eval_times: Numeric vector of time points. If NULL, uses all unique event times.
weight_result: Output from estimate_weights().
censoring_formula: Formula for censoring score model.
censoring_control: Control parameters for survreg(). Default list(maxiter = 350).

Details

Estimates counterfactual survival function for each treatment group j: $$S^{(j)}_w(t) = 1 - \frac{\sum_i w_i I(A_i=j) \delta_i I(T_i \leq t) / K_c^{(j)}(T_i, X_i)}{\sum_i w_i I(A_i=j)}$$