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An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials
2023-06-22
health informatics
Title + abstract only
View on medRxiv
Show abstract
Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management s...
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