Open Access
September 2017 Dynamic prediction for multiple repeated measures and event time data: An application to Parkinson’s disease
Jue Wang, Sheng Luo, Liang Li
Ann. Appl. Stat. 11(3): 1787-1809 (September 2017). DOI: 10.1214/17-AOAS1059


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.


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Jue Wang. Sheng Luo. Liang Li. "Dynamic prediction for multiple repeated measures and event time data: An application to Parkinson’s disease." Ann. Appl. Stat. 11 (3) 1787 - 1809, September 2017.


Received: 1 July 2016; Revised: 1 March 2017; Published: September 2017
First available in Project Euclid: 5 October 2017

zbMATH: 1380.62260
MathSciNet: MR3709578
Digital Object Identifier: 10.1214/17-AOAS1059

Keywords: Area under the ROC curve , clinical trial , failure time , latent trait model

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.11 • No. 3 • September 2017
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