| Recurrent event data are frequently encountered in clinical and observational studies related to biomedical science, econometrics, reliability and demography. In some situations, recurrent events serve as important measurements for evaluating disease progression, health deterioration, or insurance risk. In statistical literature, noninformative censoring is typically assumed when statistical methods and theories are developed for analyzing recurrent event data. In many applications, however, there may exist a terminal event such as death that stops the follow-up, and the terminal event is strongly correlated with the recurrent event process. This dissertation considers joint modeling and analysis of recurrent event and terminal event data.;First, we propose a joint model of the recurrent event process and the terminal event through a common subject-specific latent variable, in which the proportional intensity model is used for modeling the recurrent event process and the additive hazards model is used for modeling the terminal event time. Next, we consider joint modeling of the recurrent event process and the terminal event time through latent variables, in which the proportional intensity model is used for modeling the recurrent event process and the additive-multiplicative hazards model is used for the terminal event time. Finally, we study a marginal mixed effect rates model for recurrent event data in the presence of a terminal event. The mixed effect rates model displays a variety of patterns for the effects of covariates on the rate function, and allows for both proportional and converging effects.;Estimating procedures and asymptotic properties for the parameters of the three models are established. Simulation studies demonstrate that the proposed methods perform well for practical settings. The applications to hospitalization data for heart failure patients from the University of Virginia Health System are also provided for all three models. |
