Dynamic Prediction for Multiple Repeated Measures and Event Time Data: An Application to Parkinson's Disease

TL;DR

This paper introduces a joint Bayesian model combining multilevel latent trait modeling and survival analysis to predict disease progression and event timing in Parkinson's disease patients, aiding clinical decision-making.

Contribution

The paper develops a novel joint modeling framework that integrates multiple longitudinal outcomes with survival data for dynamic prediction in neurodegenerative diseases.

Findings

Model accurately predicts disease progression in simulations.

Applied successfully to Parkinson's disease clinical trial data.

Provides a tool for personalized prognosis and treatment planning.

Abstract

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.