Joint Modeling of Multivariate Longitudinal Measurements and Survival Data with Applications to Parkinson's Disease

• Activity Number: 308
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Sheng Luo*+ and Bo He
• Companies: The University of Texas Health Science Center at Houston and The University of Texas at Houston
• Keywords: Joint model ; Item-response theory ; Latent variable ; Markov Chain Monte Carlo ; Mixed model

Abstract

In many clinical trials studying neurological 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, dropout. In this article, we develop a joint model which consists of a multilevel item response theory model (MLIRT) 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 linked together two models. The model inference is conducted using a Bayesian framework via Markov Chain Monte Carlo simulation. 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.