Bayesian analysis of multi-type recurrent events and dependent termination with nonparametric covariate functions. (December 2017)

• Record Type: Journal Article
• Authors: Lin, Li-An; Luo, Sheng; Chen, Bingshu E; Davis, Barry R
• Is Part Of: Statistical methods in medical research. Volume 26:Number 6(2017)
• Page Start: 2869
• Page End: 2884
• Publication Date: 2017-12
• Subjects: Recurrent events -- joint model -- multivariate frailty model -- Markov Chain Monte Carlo -- hypertension; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280215613378
• Languages: English
• ISSNs: 0962-2802

Abstract

Multi-type recurrent event data occur frequently in longitudinal studies. Dependent termination may occur when the terminal time is correlated to recurrent event times. In this article, we simultaneously model the multi-type recurrent events and a dependent terminal event, both with nonparametric covariate functions modeled by B-splines. We develop a Bayesian multivariate frailty model to account for the correlation among the dependent termination and various types of recurrent events. Extensive simulation results suggest that misspecifying nonparametric covariate functions may introduce bias in parameter estimation. This method development has been motivated by and applied to the lipid-lowering trial component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial.