An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials.

ABSTRACT: Randomized clinical 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 strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interi

SUBMITTER: Oikonomou EK 

PROVIDER: S-EPMC10673945 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature