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Title: A time-varying joint frailty-copula model for analyzing recurrent events and a terminal event Authors:  Ming Wang - Pennsylvania State University (United States) [presenting]
Abstract: Recurrent events could be stopped by a terminal event, which commonly occurs in biomedical and clinical studies. In this situation, the non-informative censoring assumption could be failed because of potential dependency between these two event processes, leading to invalid inference if analyzing recurrent events alone. The joint frailty model is widely used to jointly model these two processes. Recurrent events and terminal event processes are conditionally independent given the subject-level frailty. Furthermore, the correlation between the terminal event and the recurrent events is constant over time. We are motivated by the Cardiovascular Health Study (CHS). Both the correlation and the latent health status might change during the follow-up period. We propose a time-varying joint frailty-copula model to relax these two assumptions under the Bayesian framework. The simulation studies show that the performance of our proposal, compared with the joint frailty model, has smaller absolute bias and mean squared error. Finally, we apply our method to analyze the CHS data potential to identify risk factors for myocardial infarction and stroke. We also quantify the correlation between these two types of events and all-cause death.