In many medical studies, markers are contingent on recurrent events and the cumulative markers are usually of interest. However, the recurrent event process is often interrupted by a dependent terminal event, such as death. In this article, we propose a joint modeling approach for analyzing marker data with informative recurrent and terminal events.

This approach introduces a shared frailty to specify the explicit dependence structure among the markers, the recurrent and terminal events. Estimation procedures are developed for the model parameters and the degree of dependence, and the asymptotic properties of the proposed estimators are established. In addition, a prediction of the covariate-specific cumulative markers is provided. The finite sample performance of the proposed estimators is examined through simulation studies. An application to a medical cost study of chronic heart failure patients from the University of Virginia Health System is illustrated.

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