B0960
Title: Regression analysis of multivariate recurrent event data allowing time-varying dependence
Authors: Wen Li - The University of Texas (United States) [presenting]
Mohammad Rahbar - The University of Texas Health Science Center (United States)
Sean Savitz - The University of Texas Health Science Center (United States)
Jing Zhang - The University of Texas Health Science Center (United States)
Sori Lundin - The University of Texas Health Science Center (United States)
Amirali Tahanan - The University of Texas Health Science Center (United States)
Jing Ning - The University of Texas MD Anderson Cancer Center (United States)
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 complicated correlations among different types of recurrent events. We developed an expectation-maximization (EM) 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 a cohort of stroke patients identified from 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.
