Bayesian Modeling of Recurrent Event Data with Dependent Censoring

Abstract

The recurrent-event setting, where the subjects experience multiple occurrences of the event of interest, are encountered in many biomedical applications. In analyzing recurrent event data, non informative censoring is often assumed for the implementation of statistical methods. However, when a terminating event such as death serves as part of the censoring mechanism, validity of the censoring assumption may be violated because recurrence can be a powerful risk factor for death. We consider joint modeling of recurrent event process and terminating event under a Bayesian framework in which a shared frailty is used to model the association between the intensity of the recurrent event process and the hazard of the terminating event. Our proposed model is implemented on data from a well-known cancer study.

Keywords:

• Bayesian analysis
• Dependent censoring
• Frailty
• Log-skew-t
• Recurrent event

Mathematics Subject Classification:

• 62N01
• 62P10
• 62F15

Acknowledgments

The authors thank the Editor for timely processing of the manuscript. They also gratefully acknowledge the thorough reading of an earlier version by the Associate Editor and two anonymous referees whose several valuable suggestions helped improve the presentation considerably.