var_surv_weibull_analytical: Compute Analytical M-Estimation Variance for Binary Treatment...

In PSsurvival: Propensity Score Methods for Survival Analysis

Compute Analytical M-Estimation Variance for Binary Treatment Survival Functions

Description

Computes analytical variance estimates using M-estimation for binary treatment. Calculates variances for S^(0)(t), S^(1)(t), and their difference S^(1)(t) - S^(0)(t).

Usage

var_surv_weibull_analytical(surv_result)

Arguments

surv_result

Output from surv_weibull() with binary treatment (2 levels).

Details

Implements M-estimation variance for binary treatment survival functions. For each group j:

I_j = \frac{1}{E_\tau}(I_{\tau,j} + I_{\theta_j} + I_{\gamma_j} + I_{\beta,j})

Var(S^{(j)}) = \sum I_j^2 / n^2

For the difference:

I_{diff} = \frac{1}{E_\tau}(I_{\tau,diff} + I_{\theta_1} - I_{\theta_0} + I_{\gamma_1} - I_{\gamma_0} + I_{\beta,diff})

Var(S^{(1)} - S^{(0)}) = \sum I_{diff}^2 / n^2

Value

List containing:

var_matrix	Matrix [time x 3] of variances: [var(S0), var(S1), var(S1-S0)].
se_matrix	Matrix [time x 3] of standard errors: [se(S0), se(S1), se(S1-S0)].
influence_components	List of Itheta and Igamma arrays for delta variance.
Etau	Normalization constant.
n	Sample size after trimming.

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