Regression Analysis of Multivariate Recurrent Event Data Allowing Time-Varying Dependence With Application to Stroke Registry Data

Authors

Wen Li
Mohammad H Rahbar
Sean I Savitz
Jing Zhang
Sori Kim Lundin
Amirali Tahanan
Jing Ning

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