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).
var_surv_weibull_analytical(surv_result)List containing:
Matrix [time x 3] of variances: [var(S0), var(S1), var(S1-S0)].
Matrix [time x 3] of standard errors: [se(S0), se(S1), se(S1-S0)].
List of Itheta and Igamma arrays for delta variance.
Normalization constant.
Sample size after trimming.
Output from surv_weibull() with binary treatment (2 levels).
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: $$I_{diff} = \frac{1}{E_\tau}(I_{\tau,diff} + I_{\theta_1} - I_{\theta_0} + I_{\gamma_1} - I_{\gamma_0} + I_{\beta,diff})$$ $$Var(S^{(1)} - S^{(0)}) = \sum I_{diff}^2 / n^2$$