  In many clinical trials studying neurodegenerative diseases such as
Parkinson's disease (PD), multiple longitudinal outcomes are collected to fully
explore the multidimensional impairment caused by this disease. If the outcomes
deteriorate rapidly, patients may reach a level of functional disability
sufficient to initiate levodopa therapy for ameliorating disease symptoms. An
accurate prediction of the time to functional disability is helpful for
clinicians to monitor patients' disease progression and make informative
medical decisions. In this article, we first propose a joint model that
consists of a semiparametric multilevel latent trait model (MLLTM) for the
multiple longitudinal outcomes, and a survival model for event time. The two
submodels are linked together by an underlying latent variable. We develop a
Bayesian approach for parameter estimation and a dynamic prediction framework
for predicting target patients' future outcome trajectories and risk of a
survival event, based on their multivariate longitudinal measurements. Our
proposed model is evaluated by simulation studies and is applied to the DATATOP
study, a motivating clinical trial assessing the effect of deprenyl among
patients with early PD.
