Generalized Method of Moments Approaches for Analyzing Recurrent Event Data

Music City Center 

In this presentation, we introduce generalized method of moments (GMM) approaches for analyzing recurrent event data with informative censoring. Our framework employs a shared frailty model to account for the correlation between the recurrent event process and censoring time, allowing the frailty variable to be covariate-dependent. Unlike traditional shared-frailty proportional intensity models, our approach is based on rate models, enabling non-proportional rate functions across different covariate groups over time. The proposed GMM methods are robust, as they do not rely on Poisson process assumptions for recurrent events or specific distributional assumptions for frailty and censoring times. We establish the large-sample properties of our methods and evaluate their finite-sample performance through extensive simulation studies. Finally, we apply the proposed methods to a real dataset.

Generalized method of moments

Recurrent event data

Informative censoring

Covariate-dependent frailty 

International Chinese Statistical Association