Bayesian approach for flexible modeling of semicompeting risks data. (2nd October 2014)

• Record Type: Journal Article
• Authors: Han, Baoguang; Yu, Menggang; Dignam, James J.; Rathouz, Paul J.
• Is Part Of: Statistics in medicine. Volume 33:Number 29(2014)
• Page Start: 5111
• Page End: 5125
• Publication Date: 2014-10-02
• Subjects: illness–death -- Markov chain Monte Carlo -- random effects -- semicompeting risks; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6313
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Semicompeting risks data arise when two types of events, non‐terminal and terminal, are observed. When the terminal event occurs first, it censors the non‐terminal event, but not vice versa. To account for possible dependent censoring of the non‐terminal event by the terminal event and to improve prediction of the terminal event using the non‐terminal event information, it is crucial to model their association properly. Motivated by a breast cancer clinical trial data analysis, we extend the well‐known illness–death models to allow flexible random effects to capture heterogeneous association structures in the data. Our extension also represents a generalization of the popular shared frailty models that usually assume that the non‐terminal event does not affect the hazards of the terminal event beyond a frailty term. We propose a unified Bayesian modeling approach that can utilize existing software packages for both model fitting and individual‐specific event prediction. The approach is demonstrated via both simulation studies and a breast cancer data set analysis. Copyright © 2014 John Wiley & Sons, Ltd.