Adaptive enrichment trials aim to identify and recruit participants most likely to benefit from treatment based on evolving biomarker evidence, with the goal of informing individualized treatment recommendations. Bayesian methods are well suited to these designs because they allow external information to be incorporated in a principled manner. In practice, prior studies often provide only summary-level information, with subgroup-specific estimates unavailable due to design or privacy constraints. Existing dynamic borrowing approaches therefore rely on aggregate measures, such as the average treatment effect, and implicitly assume that historical information maps directly onto model parameters. In adaptive enrichment settings aimed at identifying individualized treatment effects, however, subgroup-specific treatment parameters are not identifiable when only marginal historical effects are available. To address this gap, we propose a Bayesian adaptive enrichment design that borrows information from external studies using a normalized power prior anchored on one or more summary measures, such as the average treatment effect. Interim analyses use posterior probabilities to guide early stopping for efficacy or futility, or to continue recruitment within promising biomarker-defined subgroups. Simulation studies evaluate operating characteristics across historical bias, sample size, and prior informativeness. Together with a motivating future trial in obstructive sleep apnea, the results show efficiency gains versus non-borrowing designs, including improved power, earlier stopping, and reduced expected sample size.

翻译：自适应富集试验旨在根据不断演变的生物标志物证据，识别并招募最有可能从治疗中获益的受试者，其目标是为个体化治疗推荐提供依据。贝叶斯方法非常适合此类设计，因为它们允许以原则性方式纳入外部信息。在实践中，先前的研究通常仅提供汇总层面的信息，由于设计或隐私限制，无法获得亚组特异性估计值。因此，现有的动态借用方法依赖于汇总指标（如平均治疗效果），并隐含地假设历史信息直接映射到模型参数。然而，在旨在识别个体化治疗效果的自适应富集情境中，当仅可获得边际历史效应时，亚组特异性治疗参数是不可识别的。为弥补这一空白，我们提出了一种贝叶斯自适应富集设计，该设计使用基于一个或多个汇总指标（如平均治疗效果）的归一化幂先验，从外部研究借用信息。期中分析使用后验概率来指导因疗效或无效而提前终止，或在有前景的生物标志物定义的亚组内继续招募。模拟研究评估了在不同历史偏倚、样本量和先验信息性下的操作特性。结合一项关于阻塞性睡眠呼吸暂停的激励性未来试验，结果表明，相较于非借用设计，该设计获得了效率提升，包括检验功效提高、更早停止以及预期样本量减少。