Estimate censoring scores P(C >= T | X) using Weibull or Cox models fit separately within each treatment group. Estimate Censoring Scores Using Weibull Regression
estimate_censoring_score_weibull(
data,
time_var,
treatment_var,
formula,
control = list(maxiter = 350)
)List with class "censoring_score_weibull":
Fitted survreg objects by treatment level.
P(C >= T_i | Z_i, X_i) for observed treatment.
(n x J) matrix of P(C >= T_i | Z=j, X_i).
Number of treatment levels.
Sorted treatment values.
"weibull".
Transformed parameters (theta, gamma, coef,
vcov, scale) by treatment level.
(n x J) matrix of linear predictors.
Data frame.
Name of time variable.
Name of treatment variable.
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 parameters for survreg(). Default
list(maxiter = 350).
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 \(\theta_j = -\beta_j/\sigma_j\), \(\gamma_j = 1/\sigma_j\).