While-alive regression analysis of composite survival endpoints

Composite endpoints are frequently used in clinical trials to enhance the event rate and improve the statistical power. In the presence of a terminal event, the while-alive cumulative frequency measure offers a useful alternative to define composite survival outcomes, by relating the average event rate to the survival time. Although non-parametric methods have been proposed for two-sample comparisons, limited attention has been given to regression methods that directly address time-varying association effects in while-alive measures. We address this gap by developing a regression framework for exposure-weighted while-alive measures for composite survival outcomes that include a terminal component event. Our regression approach uses splines to model time-varying association between covariates and a generalized while-alive loss rate of all component events, and can be applied to both independent and clustered data. We derive the asymptotic properties of the regression estimator under both independent data and cluster-correlated data settings, and study the operating characteristics of our methods through simulations. Finally, we apply our regression method to analyze data two randomized clinical trials. The proposed methods are implemented in the WAreg R package.