多靶点联合治疗有可能改善癌症患者的治疗效果。与单一治疗相比，有针对性的联合治疗会导致越来越多的亚组和复杂的生物标志物评估效果，使得在临床试验中评估疗效更加困难。因此，有必要开发创新的临床试验设计来探索针对不同亚组的联合治疗的疗效，并识别哪些患者更可能从调查联合治疗中获益。我们提出了一种名为“IBIS”的统计工具，用于识别基于生物标记物的亚组，并将其应用于富集设计框架。IBIS包含三个主要元素：亚组分割、疗效评估和亚组识别。我们首先基于生物标志物水平列举所有可能的亚组分割。然后，使用Jensen-Shannon分歧来区分高疗效和低疗效亚组，并采用贝叶斯分层模型（BHM）在这两个子集中借用信息进行疗效评估。关于亚组识别，构建了一个基于贝叶斯因子的假设检验框架。该框架还在“启动/禁止”决策和富集特定人群方面发挥关键作用。进行模拟研究以评估所提出的方法。在估计性能方面，IBIS的准确性和精度可以达到所需水平。关于亚组识别和人群富集，与传统方法相比，所提出的IBIS具有优越和稳健的特征。此外，还提供了一个关于如何在IBIS框架下获取自适应富集设计的设计参数的例子。IBIS有潜力成为基于生物标记物的亚组识别和人群富集在针对性联合治疗的临床试验中使用的有用工具。 ©2023.作者。

Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult for efficacy evaluation in clinical trials. Therefore, it is necessary to develop innovative clinical trial designs to explore the efficacy of targeted combination therapy in different subgroups and identify patients who are more likely to benefit from the investigational combination therapy.We propose a statistical tool called 'IBIS' to Identify BIomarker-based Subgroups and apply it to the enrichment design framework. The IBIS contains three main elements: subgroup division, efficacy evaluation and subgroup identification. We first enumerate all possible subgroup divisions based on biomarker levels. Then, Jensen-Shannon divergence is used to distinguish high-efficacy and low-efficacy subgroups, and Bayesian hierarchical model (BHM) is employed to borrow information within these two subsets for efficacy evaluation. Regarding subgroup identification, a hypothesis testing framework based on Bayes factors is constructed. This framework also plays a key role in go/no-go decisions and enriching specific population. Simulation studies are conducted to evaluate the proposed method.The accuracy and precision of IBIS could reach a desired level in terms of estimation performance. In regard to subgroup identification and population enrichment, the proposed IBIS has superior and robust characteristics compared with traditional methods. An example of how to obtain design parameters for an adaptive enrichment design under the IBIS framework is also provided.IBIS has the potential to be a useful tool for biomarker-based subgroup identification and population enrichment in clinical trials of targeted combination therapy.© 2023. The Author(s).