Bayesian integration of longitudinal and survival outcomes in Alzheimer's disease prediction

We introduced a novel joint modeling framework integrating multivariate longitudinal outcomes (Mini-Mental State Examination and Clinical Dementia Rating Sum of Boxes) with survival data to predict Alzheimer's disease dementia onset and progression. We validated the framework across complementary datasets: Alzheimer's Disease Neuroimaging Initiative (training) and National Alzheimer's Coordinating Center (NACC; validation), with NACC providing a demographically diverse population to assess generalizability. The model enhanced predictive accuracy using Bayesian model averaging, which synthesizes insights across multiple models to reduce uncertainty and improve robustness. The model demonstrated consistent and clinically relevant performance, supporting its applicability for early intervention, precision medicine, and clinical trial design.