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

Published

Journal Article

© 2019, © 2019 International Biometric Society–Chinese Region. 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.

Full Text

Duke Authors

Cited Authors

  • Ren, X; Wang, J; Luo, S

Published Date

  • January 1, 2019

Published In

  • Biostatistics and Epidemiology

Electronic International Standard Serial Number (EISSN)

  • 2470-9379

International Standard Serial Number (ISSN)

  • 2470-9360

Digital Object Identifier (DOI)

  • 10.1080/24709360.2019.1693198

Citation Source

  • Scopus