title: A Bayesian approach to joint analysis of multivariate longitudinal data and parametric accelerated failure time.
abstract: Impairment caused by Parkinson's disease (PD) is multidimensional (e.g., sensoria, functions, and cognition) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of PD use multiple categorical and continuous longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we consider a joint random-effects model for the correlated outcomes. A multilevel item response theory model is used for the multivariate longitudinal outcomes and a parametric accelerated failure time model is used for the failure time because of the violation of proportional hazard assumption. These two models are linked via random effects. The Bayesian inference via MCMC is implemented in 'BUGS' language. Our proposed method is evaluated by a simulation study and is applied to DATATOP study, a motivating clinical trial to determine if deprenyl slows the progression of PD.
authors.affiliation: Division of Biostatistics, University of Texas School of Public Health, 1200 Pressler St., Houston, TX 77030, U.S.A.
authors.forename: Sheng
authors.initials: S
authors.lastname: Luo
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D018450:Disease Progression; D004359:Drug Therapy, Combination; D006801:Humans; D008137:Longitudinal Studies / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D010300:Parkinson Disease / Q000188:drug therapy*; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012642:Selegiline / Q000627:therapeutic use; D024505:Tocopherols / Q000627:therapeutic use
chemical_list: D012642:Selegiline; D024505:Tocopherols
keywords: Markov chain Monte Carlo; clinical trial; failure time; item response theory; latent variable