Authors
Henry Bayly, Lindsay Salvati, Steven Lenio, Jesse Mez, Michael L Alosco, Yorghos Tripodis
Published in
JAR life. Volume 15. Pages 100081. Epub Aug 08, 2026.
Abstract
Prevention trials for Alzheimer's Disease face significant challenges due to the slow and uncertain rate of cognitive decline in asymptomatic, amyloid-positive individuals. Biomarker positivity alone does not guarantee clinically meaningful progression, often leaving studies underpowered.
Evaluate model-based operational trial enrichment within an amyloid-positive, cognitively normal subgroup.
Trained on a small cohort from the National Alzheimer's Coordinating Center (N = 113), with external validation using baseline data of a similar small cohort from the Alzheimer's Disease Neuroimaging Initiative (N = 161). Gradient-boosted decision trees estimated progression risk and projected power under alternative enrollment strategies. Predictors included neuropsychological scores, demographics, medical history, and ApoE ε4 genotype. The primary outcome was clinical progression to cognitive impairment within 1- and 3-year follow-up windows.
In the independent testing set, the model successfully increased the effective prevalence of the outcome. The Positive Predictive Value for the 1-year window was 0.27 (a 35% relative increase over the 0.20 baseline prevalence) and 0.43 for the 3-year window (a 23% relative increase over the 0.35 baseline). Power simulations for an emulated trial (N = 500) demonstrated that this enrichment strategy consistently increased statistical power, potentially reducing the required sample size to achieve 80% power by up to 32% for small treatment effect sizes.
This operational methodology functions as a screening filter to optimize event rates within a highly specific, preselected trial population.
PMID:
42604096
Bibliographic data and abstract were imported from PubMed on 16 Aug 2026.
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