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A Bayesian approach to joint analysis of longitudinal measurements and competing risks failure time data
Author(s) -
Hu Wenhua,
Li Gang,
Li Ning
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3562
Subject(s) - censoring (clinical trials) , computer science , bayesian probability , inference , bayesian inference , event (particle physics) , missing data , causal inference , random effects model , data mining , econometrics , statistics , machine learning , artificial intelligence , mathematics , medicine , meta analysis , physics , quantum mechanics
In this paper, we develop a Bayesian method for joint analysis of longitudinal measurements and competing risks failure time data. The model allows one to analyze the longitudinal outcome with nonignorable missing data induced by multiple types of events, to analyze survival data with dependent censoring for the key event, and to draw inferences on multiple endpoints simultaneously. Compared with the likelihood approach, the Bayesian method has several advantages. It is computationally more tractable for high‐dimensional random effects. It is also convenient to draw inference. Moreover, it provides a means to incorporate prior information that may help to improve estimation accuracy. An illustration is given using a clinical trial data of scleroderma lung disease. The performance of our method is evaluated by simulation studies. Copyright © 2009 John Wiley & Sons, Ltd.