Dynamic Prediction Using Functional Latent Trait Joint Models for Multivariate Longitudinal Outcomes: An Application to Parkinson's Disease.

Abstract

The progressive and multifaceted nature of Parkinson's disease (PD) calls for the integration of diverse data types, including continuous, ordinal, and binary, in longitudinal studies for a comprehensive understanding of symptom progression and disease trajectory. Significant terminal events, such as severe disability or mortality, highlight the need for joint modeling approaches that simultaneously address multivariate outcomes and time-to-event data. We introduce functional latent trait model-joint model (FLTM-JM), a novel joint modeling framework based on the functional latent trait model (FLTM), to jointly analyze multivariate longitudinal data and survival outcomes. The FLTM component leverages a non-parametric, function-on-scalar regression framework, enabling flexible modeling of complex relationships between covariates and patient outcomes over time. This joint modeling approach supports dynamic, subject-specific predictions, offering valuable insights for personalized treatment strategies. Applied to Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) data from the Parkinson's Progression Markers Initiative (PPMI), our model effectively identifies the influence of key covariates and demonstrates the utility of dynamic predictions in clinical decision-making. Extensive simulation studies validate the accuracy, robustness, and computational efficiency of FLTM-JM, even under model misspecification.