title: A Bayesian approach to joint analysis of longitudinal measurements and competing risks failure time data.
abstract: 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.
authors.affiliation: Bristol-Myers Squibb, Wallingford, CT 06450, U.S.A. wenhua.hu@bms.com
authors.forename: Wenhua
authors.initials: W
authors.lastname: Hu
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.forename: Ning
authors.initials: N
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005500:Follow-Up Studies; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D017563:Lung Diseases, Interstitial / Q000628:therapy*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D017211:Treatment Failure; D016896:Treatment Outcome