Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
Bayesian integration of longitudinal and survival outcomes in Alzheimer's disease prediction.
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Zou Haotian, Lutz Michael W, Welsh-Bohmer Kathleen, Luo Sheng
Аннотация
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 using the C-index, a measure of discrimination.
RESULTS: Compared to the composite model, BMA improved prediction accuracy. The C-index was 0.777 (stacking) and 0.771 (pseudo-BMA+) in ADNI and 0.743 and 0.738 in NACC.
DISCUSSION: This framework offers a robust tool for personalized medicine, enabling accurate predictions and enhanced generalizability across diverse populations.
HIGHLIGHTS: 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.
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 using the C-index, a measure of discrimination.
RESULTS: Compared to the composite model, BMA improved prediction accuracy. The C-index was 0.777 (stacking) and 0.771 (pseudo-BMA+) in ADNI and 0.743 and 0.738 in NACC.
DISCUSSION: This framework offers a robust tool for personalized medicine, enabling accurate predictions and enhanced generalizability across diverse populations.
HIGHLIGHTS: 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.