Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, USA.
Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas, USA.
Stat Med. 2021 May 30;40(12):2893-2921. doi: 10.1002/sim.8946. Epub 2021 Mar 26.
Given the Food and Drug Administration's (FDA's) acceptance of master protocol designs in recent guidance documents, the oncology field is rapidly moving to address the paradigm shift to molecular subtype focused studies. Identifying new "marker-based" treatments requires new methodologies to address the growing demand to conduct clinical trials in smaller molecular subpopulations, identify effective treatment and marker interactions, and control for false positives. We introduce our methodology, Hierarchical Bayesian Clustering Design of Multiple Biomarker Subgroups (HCOMBS), a two-stage umbrella Phase II design with effect size clustering and information borrowing across multiple biomarker-treatment pairs. HCOMBS was designed to reduce required sample size, differentiate between varying effect sizes, and control for operating characteristics in the multi-arm setting. When compared to independently applied Simon's Optimal two-stage design, we showed through simulations that HCOMBS required less participants per treatment arm with a well-controlled family-wise error rate and desirable marginal power. Additionally, HCOMBS features a statistical approach that simultaneously conducts clustering and hypothesis testing in one step. We also applied the proposed design on the alliance brain metastases umbrella trial.
鉴于食品和药物管理局（FDA）在最近的指导文件中接受了主方案设计，肿瘤学领域正在迅速采取行动，以应对向分子亚型为重点的研究的范式转变。确定新的“基于标志物”的治疗方法需要新的方法来满足在更小的分子亚群中进行临床试验、确定有效治疗和标志物相互作用以及控制假阳性的需求。我们介绍了我们的方法，即多标志物亚组分层贝叶斯聚类设计（HCOMBS），这是一种两阶段的伞式 II 期设计，具有效应大小聚类和跨多个标志物-治疗对的信息借用。HCOMBS 的设计目的是减少所需的样本量，区分不同的效应大小，并在多臂环境中控制操作特征。通过模拟，我们与独立应用的 Simon 最优两阶段设计进行了比较，结果表明 HCOMBS 每个治疗臂所需的参与者较少，具有良好控制的总体错误率和理想的边缘功效。此外，HCOMBS 具有一种统计方法，可在一步中同时进行聚类和假设检验。我们还将拟议的设计应用于联盟脑转移伞式试验。
