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Significant terminal events, such as severe disability or mortality, highlight the need for joint modeling approaches that simultaneously address multivariate outcomes and time\u2010to\u2010event data. We introduce functional latent trait model\u2010joint model (FLTM\u2010JM), 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\u2010parametric, function\u2010on\u2010scalar regression framework, enabling flexible modeling of complex relationships between covariates and patient outcomes over time. This joint modeling approach supports dynamic, subject\u2010specific predictions, offering valuable insights for personalized treatment strategies. Applied to Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS\u2010UPDRS) 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\u2010making. Extensive simulation studies validate the accuracy, robustness, and computational efficiency of FLTM\u2010JM, even under model misspecification.<\/jats:p>","DOI":"10.1002\/sim.70285","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T05:34:51Z","timestamp":1760679291000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Prediction Using Functional Latent Trait Joint Models for Multivariate Longitudinal Outcomes: An Application to Parkinson's Disease"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0602-5861","authenticated-orcid":false,"given":"Mohammad","family":"Samsul Alam","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Bioinformatics Duke University  Durham North Carolina 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