ArticlemedRxiv : the preprint server for health sciences2025
A digital twin methodology using real patient data for sample size reduction in Alzheimer's disease randomized controlled clinical trials.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionRecruitment for Alzheimer's disease randomized controlled trials (RCTs) is difficult and expensive. To reduce RCT sample sizes, our Digital Twin Trial (DTT) methodology combines an interpretable cognitive decline prediction model with prediction-powered inference.
methodsFor DTT participants, our model identifies similar individuals ("Digital Twins") from a retrospective database and uses their cognitive scores to predict decline. Predictions adjust observed scores, reducing variance within treatment groups. We simulated 18-month DTTs and standard RCTs using mixed effects models of decline in Alzheimer's Disease Neuroimaging Initiative subjects meeting lecanemab's Phase 3 inclusion criteria.
resultsPredicted and observed change in Clinical Dementia Rating Sum-of-Boxes correlated at DISCUSSION: DTTs could reduce recruitment and cost burdens. Model interpretability could help clinicians trust individualized prognoses.
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