Joint Scale-Change Models for Recurrent Events and Failure Time

• Activity Number: 100
• Type: Invited
• Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Gongjun Xu and Sy Han Chiou and Chiung-Yu Huang* and Mei-Cheng Wang and Jun Yan
• Companies: University of Minnesota and Harvard and The Johns Hopkins University and The Johns Hopkins University and University of Connecticut
• Keywords: Accelerated failure time model ; Frailty ; Informative censoring ; Marginal models ; Semiparametric methods

Abstract

Recurrent event data arise frequently in various fields such as biomedical sciences, public health, engineering, and social sciences. In many instances, the observation of the recurrent event process can be stopped by the occurrence of a correlated failure event, such as treatment failure and death. In this article, we propose a joint scale-change model for the recurrent event process and the failure time, where a shared frailty variable is used to model the association between the two types of outcomes. In contrast to the popular Cox-type joint modeling approaches, the regression parameters in the proposed joint scale-change model have marginal interpretations. The proposed approach is robust in the sense that no parametric assumption is imposed on the distribution of the unobserved frailty and that we do not need the strong Poisson-type assumption for the recurrent event process. To estimate the corresponding variances of the estimators, we develop a computationally efficient resampling-based procedure. Simulation studies and an analysis of hospitalization data from the Danish Psychiatric Central Register illustrate the performance of the proposed method.