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 co

SUBMITTER: Samsul Alam M 

PROVIDER: S-EPMC12614809 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature