Developing targeted therapies based on patients’ baseline characteristics and genomic profiles such as biomarkers has gained growing interests in recent years. Depending on patients’ clinical characteristics, the expression of specific biomarkers or their combinations, different patient subgroups could respond differently to the same treatment. An ideal design, especially at the proof of concept stage, should search for such subgroups and make dynamic adaptation as the trial goes on. When no prior knowledge is available on whether the treatment works on the all-comer population or only works on the subgroup defined by one biomarker or several biomarkers, it is necessary to incorporate the adaptive estimation of the heterogeneous treatment effect to the decision-making at interim analyses. To address this problem, we propose an Adaptive Subgroup-Identification Enrichment Design, ASIED, to simultaneously search for predictive biomarkers, identify the subgroups with differential treatment effects, and modify study entry criteria at interim analyses when justified. More importantly, we construct robust quantitative decision-making rules for population enrichment when the interim outcomes are heterogeneous in the context of a multilevel target product profile, which defines the minimal and targeted levels of treatment effect. Through extensive simulations, the ASIED is demonstrated to achieve desirable operating characteristics and compare favorably against alternatives.

近年来，基于患者的基线特征和基因组特征（如生物标志物）开发靶向治疗越来越受到关注。根据患者的临床特征、特定生物标志物的表达或其组合，不同的患者亚组可能对相同的治疗有不同的反应。一个理想的设计，尤其是在概念验证阶段，应该寻找这样的亚组，并随着试验的进行进行动态调整。当没有关于治疗是否适用于所有人群或仅适用于由一种生物标志物或几种生物标志物定义的亚组的先验知识时，有必要将异质性治疗效果的适应性估计纳入决策中期分析。为了解决这个问题，我们提出了一种适应性亚组识别富集设计 ASIED，以同时搜索预测性生物标志物，识别具有不同治疗效果的亚组，并在合理的中期分析中修改研究进入标准。更重要的是，当中期结果在多层次目标产品概况的背景下是异质的，我们构建了稳健的人口富集定量决策规则，这定义了治疗效果的最小和目标水平。通过广泛的模拟，ASIED 被证明可以实现理想的操作特性，并与替代方案进行比较。并在合理的情况下在中期分析中修改研究进入标准。更重要的是，当中期结果在多层次目标产品概况的背景下是异质的，我们构建了稳健的人口富集定量决策规则，这定义了治疗效果的最小和目标水平。通过广泛的模拟，ASIED 被证明可以实现理想的操作特性，并与替代方案进行比较。并在合理的情况下在中期分析中修改研究进入标准。更重要的是，当中期结果在多层次目标产品概况的背景下是异质的，我们构建了稳健的人口富集定量决策规则，这定义了治疗效果的最小和目标水平。通过广泛的模拟，ASIED 被证明可以实现理想的操作特性，并与替代方案进行比较。