Online Program Home
My Program

Abstract Details

Activity Number: 570 - Joint Modeling of Longitudinal and Survival Data
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #304686 Presentation
Title: A Bayesian Approach for Semiparametric Regression Analysis of Bivariate Panel Count Data
Author(s): Chunling Wang* and Xiaoyan Lin
Companies: University of South Carolina and University of South Carolina
Keywords: Bayesian Semiparametric; Bivariate Panel Count Data; Correlation; Gibbs Sampler

Panel count data often occurs in a long-term recurrent event study, where only the occurrence counts between adjacent observation times are observed. Most traditional methods only handle panel count data for one single event. In this paper, we propose a Bayesian semiparameteric approach to analyze panel count data for two types of events. For each type of event, the proportional mean model is adopted to model the mean count of the event, where its baseline mean function is approximated by the monotone I-splines. The correlation between events are modeled by common frailty terms and a scale parameter. Unlike many frequentist estimating equation methods, our method is based on the observed likelihood and makes no assumption on the relationship between the recurrent process and the observation process. For implementation, we develop an efficient Gibbs sampler based on a novel data augmentation. Simulation studies show good estimation of the baseline mean function and the regression coefficients and the importance of including the scale parameter to flexibly accommodate the correlation between events. Finally, a skin data example is fully analyzed to illustrate the proposed method.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program