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White Paper

Applied Adaptive Design Using Subgroup Identification and Machine Learning

Applied Adaptive Design Using Subgroup Identification and Machine Learning

Applied Adaptive Design Using Subgroup Identification and Machine Learning

IQVIA
Pages 16 Pages

This paper shows how AI/ML tools can analyse phase 2 data to discover patient subgroups with distinct responses and generate synthetic phase 3 data. Traditional biostatistics require predefined subgroups and may miss multivariate interactions, but ML can identify optimal cut‑points (e.g., age 63) and multivariate factors to inform inclusion/exclusion criteria. Using adaptive designs—group sequential methods, sample‑size re‑estimation, Bayesian approaches and simulations with synthetic data—can shorten timelines, reduce costs and improve patient safety while enhancing the probability of phase‑3 success.

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