estimate_censoring_score_weibull: Censoring Score Estimation

In PSsurvival: Propensity Score Methods for Survival Analysis

Censoring Score Estimation

Description

Estimate censoring scores P(C >= T | X) using Weibull or Cox models fit separately within each treatment group. Estimate Censoring Scores Using Weibull Regression

Usage

estimate_censoring_score_weibull(
  data,
  time_var,
  treatment_var,
  formula,
  control = list(maxiter = 350)
)

Arguments

data	Data frame.
time_var	Name of time variable.
treatment_var	Name of treatment variable.
formula	Censoring model formula. Use Surv(time, censor_indicator) ~ X1 + X2 where censor_indicator = 1 indicates censoring. If event is coded canonically (event=1, censored=0), use I(1-event). Otherwise, use the appropriate transformation. Treatment is automatically removed if included.
control	Control parameters for survreg(). Default list(maxiter = 350).

Details

Fits Weibull models within each treatment group. Censoring scores computed as:

K_c^{(j)}(t, X) = \exp(-\exp(X'\theta_j) \cdot t^{\gamma_j})

where \theta_j = -\beta_j/\sigma_j, \gamma_j = 1/\sigma_j.

Value

List with class "censoring_score_weibull":

censoring_models	Fitted survreg objects by treatment level.
censoring_scores	P(C >= T_i | Z_i, X_i) for observed treatment.
censoring_matrix	(n x J) matrix of P(C >= T_i | Z=j, X_i).
n_levels	Number of treatment levels.
treatment_levels	Sorted treatment values.
model_type	"weibull".
parameters	Transformed parameters (theta, gamma, coef, vcov, scale) by treatment level.
linear_predictors_matrix	(n x J) matrix of linear predictors.

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