Dynamic prediction using joint models of longitudinal and recurrent event data: a Bayesian perspective

Abstract

In cardiovascular disease (CVD) studies, the events of interest may be recurrent (multiple occurrences from the same individual). During the study follow-up, longitudinal measurements are often available and these measurements are highly predictive of event recurrences. It is of great clinical interest to make personalized prediction of the next occurrence of recurrent events using the available clinical information, because it enables clinicians to make more informed and personalized decisions and recommendations. To this end, we propose a joint model of longitudinal and recurrent event data. We develop a Bayesian approach for model inference and a dynamic prediction framework for predicting target subjects' future outcome trajectories and risk of next recurrent event, based on their data up to the prediction time point. To improve computation efficiency, embarrassingly parallel MCMC (EP-MCMC) method is utilized. It partitions the data into multiple subsets, runs MCMC sampler on each subset, and applies random partition trees to combine the posterior draws from all subsets. Our method development is motivated by and applied to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), one of the largest CVD studies to compare the effectiveness of medications to treat hypertension.

Keywords:

• ALLHAT study
• personalized prediction
• cardiovascular disease
• parallel EP-MCMC

Acknowledgments

The authors acknowledge the Texas Advanced Computing Center (TACC) for providing high-performing computing resources.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Sheng Luo's research was supported in part by National Institutes of Health [grant numbers R01NS091307 and R56AG064803].

Notes on contributors

Xuehan Ren

Xuehan Ren is a Manager in the Biostatistics Department at Gilead Sciences in Foster City, CA, USA.

Jue Wang

Jue Wang is Senior Statistical Scientist at Genetech Inc.

Sheng Luo

Sheng Luo is Professor of Biostatistics and Bioinformatics, Duke University.