07: Bayesian Inference for Covariate-Adjusted Restricted Mean Survival Time Using Pseudo-Observations

Thomas M. Menino Convention & Exhibition Center 

Restricted mean survival time (RMST) is often used as an alternative summary measure of treatment effect to the hazard ratio, particularly when the proportional hazards assumption is violated. Although a frequentist regression model for estimating covariate-adjusted RMST based on pseudo-observations does not require modeling the survival function, this method often yields coverage rates below the nominal level in small-sample settings.
A Bayesian extension using the Bayesian generalized method of moments (GMM) generally provides coverage rates above the nominal level under the same settings. We proposed a method that modified Bayesian GMM from the perspective of general Bayesian inference, in which the uncertainty of the posterior distribution was adjusted by determining an appropriate learning rate. Numerical experiments assuming Weibull distributions revealed that the proposed method provided coverage rates closer to the nominal level than existing Bayesian methods in small-sample settings (n = 30, 50), while achieving coverage rates comparable to those of existing methods in large-sample settings (n = 200).

Survival analysis

Bayesian Inference

Restricted mean survival time

General Bayesian Inference 

Biometrics Section