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

ABSTRACT:

Introduction

Accurate prediction of Alzheimer's disease (AD) dementia onset and progression to mild cognitive impairment (MCI) is crucial for early intervention and clinical trial design. This study presents a predictive framework leveraging Bayesian model averaging (BMA) with a multivariate functional mixed model (MFMM) to integrate multivariate longitudinal outcomes and survival data.

Methods

The training cohort included 1012 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The validation cohort comprised 2087 participants from the National Alzheimer's Coordinating Center (NACC). BMA methods, including stacking and pseudo-BMA+, aggregated predictions across candidate models to enhance accuracy and robustness. Predictive performance was evaluated us

SUBMITTER: Zou H 

PROVIDER: S-EPMC12402701 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature