surv_weibull: Estimate Counterfactual Survival Functions Using Weibull...

In PSsurvival: Propensity Score Methods for Survival Analysis

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)
)

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)}

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.

PSsurvival documentation built on Dec. 9, 2025, 9:07 a.m.