title: Joint modeling of multivariate longitudinal measurements and survival data with applications to Parkinson's disease.
abstract: In many clinical trials, studying neurodegenerative diseases including Parkinson's disease (PD), multiple longitudinal outcomes are collected in order to fully explore the multidimensional impairment caused by these diseases. The follow-up of some patients can be stopped by some outcome-dependent terminal event, e.g. death and dropout. In this article, we develop a joint model that consists of a multilevel item response theory (MLIRT) model for the multiple longitudinal outcomes, and a Cox's proportional hazard model with piecewise constant baseline hazards for the event time data. Shared random effects are used to link together two models. The model inference is conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in BUGS language. Our proposed model is evaluated by simulation studies and is applied to the DATATOP study, a motivating clinical trial assessing the effect of tocopherol on PD among patients with early PD.
authors.affiliation: Division of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: He
authors.affiliation: Division of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA sheng.t.luo@uth.tmc.edu.
authors.forename: Sheng
authors.initials: S
authors.lastname: Luo
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D010300:Parkinson Disease / Q000188:drug therapy*; D024505:Tocopherols / Q000627:therapeutic use
chemical_list: D024505:Tocopherols
keywords: Markov Chain Monte Carlo; item-response theory; joint model; latent variable; mixed model