
Faculty, Staff and Student Publications
Publication Date
2-1-2024
Journal
Statistical Methods in Medical Research
Abstract
In multivariate recurrent event data, each patient may repeatedly experience more than one type of event. Analysis of such data gets further complicated by the time-varying dependence structure among different types of recurrent events. The available literature regarding the joint modeling of multivariate recurrent events assumes a constant dependency over time, which is strict and often violated in practice. To close the knowledge gap, we propose a class of flexible shared random effects models for multivariate recurrent event data that allow for time-varying dependence to adequately capture complex correlation structures among different types of recurrent events. We developed an expectation-maximization algorithm for stable and efficient model fitting. Extensive simulation studies demonstrated that the estimators of the proposed approach have satisfactory finite sample performance. We applied the proposed model and the estimating method to data from a cohort of stroke patients identified in the University of Texas Houston Stroke Registry and evaluated the effects of risk factors and the dependence structure of different types of post-stroke readmission events.
Keywords
Humans, Multivariate Analysis, Routinely Collected Health Data, Regression Analysis, Computer Simulation, Stroke, Models, Statistical, Recurrence, Expectation–maximization algorithm, multivariate recurrent events, random effects, stroke, survival analysis, time-varying dependence
DOI
10.1177/09622802231226330
PMID
38263734
PMCID
PMC11080814
PubMedCentral® Posted Date
2-1-2025
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
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