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A joint modeling approach for analyzing marker data in the presence of a terminal event
Author(s) -
Zhou Jie,
Chen Xin,
Song Xinyuan,
Sun Liuquan
Publication year - 2021
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13260
Subject(s) - estimator , terminal (telecommunication) , covariate , event (particle physics) , computer science , joint (building) , sample (material) , statistics , data mining , machine learning , mathematics , engineering , architectural engineering , telecommunications , physics , chemistry , chromatography , quantum mechanics
In many medical studies, markers are contingent on recurrent events and the cumulative markers are usually of interest. However, the recurrent event process is often interrupted by a dependent terminal event, such as death. In this article, we propose a joint modeling approach for analyzing marker data with informative recurrent and terminal events. This approach introduces a shared frailty to specify the explicit dependence structure among the markers, the recurrent, and terminal events. Estimation procedures are developed for the model parameters and the degree of dependence, and a prediction of the covariate‐specific cumulative markers is provided. The finite sample performance of the proposed estimators is examined through simulation studies. An application to a medical cost study of chronic heart failure patients from the University of Virginia Health System is illustrated.