ArticleStatistical methods in medical research2026
Bayesian adaptive design for clinical trials with potential subgroup effects.
Article in Statistical methods in medical research, 2026. 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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5 authors.
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Abstract
Adaptive clinical trial designs increasingly aim to improve efficiency while accommodating subgroup heterogeneity, yet most existing methods fix assumptions about drug efficacy and subgroup effects. We propose a Bayesian adaptive design that explicitly models and learns from uncertainty in both components. A hierarchical mixture prior represents uncertainty about overall treatment efficacy and the magnitude of a biomarker-defined subgroup effect. Interim data are used to update these hyperparameters into posterior distributions, enabling a decision-theoretic framework that adaptively selects the optimal testing strategy among three options: continuing with the overall population, focusing on the subgroup, or conducting a joint test of both. When joint testing is chosen, the posterior information further determines the optimal allocation of Type I error between populations by selecting an evidence-based
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