Dynamic Predictions from Joint Models for Multivariate Longitudinal Measurements and Survival Data

• Activity Number: 608
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Sheng Luo* and Jue Wang
• Companies: The University of Texas Health Science Center and The University of Texas at Houston
• Keywords: Predicted probability ; Parkinson's disease ; Prognostic Modeling ; Random-effects ; Markov Chain Monte Carlo

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. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the survival event that uses the multivariate longitudinal measures from a new patient. 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.