Dynamic prediction using joint models of longitudinal and recurrent event data: a Bayesian perspective
Abstract
© 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.
Type
Journal articlePermalink
https://hdl.handle.net/10161/19679Published Version (Please cite this version)
10.1080/24709360.2019.1693198Publication Info
Ren, Xuehan; Wang, Jue; & Luo, Sheng (2019). Dynamic prediction using joint models of longitudinal and recurrent event data: a
Bayesian perspective. Biostatistics and Epidemiology. pp. 1-17. 10.1080/24709360.2019.1693198. Retrieved from https://hdl.handle.net/10161/19679.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Sheng Luo
Professor of Biostatistics & Bioinformatics

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