Efficiently analyzing large patient registries with Bayesian joint models for longitudinal and time-to-event data

Pedro Miranda Afonso*, Dimitris Rizopoulos, Anushka K. Palipana, Grace C. Zhou, Cole Brokamp, Rhonda D. Szczesniak, Elrozy Andrinopoulou

*Corresponding author for this work

Research output: Working paperPreprintAcademic

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Abstract

The joint modeling of longitudinal and time-to-event outcomes has become a popular tool in
follow-up studies. However, fitting Bayesian joint models to large datasets, such as patient
registries, can require extended computing times. To speed up sampling, we divided a patient registry dataset into subsamples, analyzed them in parallel, and combined the resulting
Markov chain Monte Carlo draws into a consensus distribution. We used a simulation study
to investigate how different consensus strategies perform with joint models. In particular,
we compared grouping all draws together with using equal- and precision-weighted averages.
We considered scenarios reflecting different sample sizes, numbers of data splits, and processor characteristics. Parallelization of the sampling process substantially decreased the time
required to run the model. We found that the weighted-average consensus distributions for
large sample sizes were nearly identical to the target posterior distribution. The proposed
algorithm has been made available in an R package for joint models, JMbayes2. This work
was motivated by the clinical interest in investigating the association between ppFEV1, a
commonly measured marker of lung function, and the risk of lung transplant or death, using data from the US Cystic Fibrosis Foundation Patient Registry (35,153 individuals with
372,366 years of cumulative follow-up). Splitting the registry into five subsamples resulted
in an 85% decrease in computing time, from 9.22 to 1.39 hours. Splitting the data and finding a consensus distribution by precision-weighted averaging proved to be a computationally
efficient and robust approach to handling large datasets under the joint modeling framework.
Original languageEnglish
PublisherarXiv
Number of pages47
Publication statusPublished - Oct 2023

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