Zhou Yanhong, Li Ruobing, Yan Fangrong, Lee J Jack, Yuan Ying
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
The Center for Drug Evaluation, Beijing, China.
Stat Biopharm Res. 2021;13(2):147-155. doi: 10.1080/19466315.2020.1811147. Epub 2020 Sep 14.
Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule - making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries. The interval 3+3 (i3+3) design is a recently proposed algorithm-based dose-finding design based on a similar decision rule with some modifications. The similarity in the appearance of the two designs has caused confusions among practitioners. In this article, we demystify the i3+3 design by elucidating its links with the BOIN design and compare their similarities and differences, as well as pros and cons. We perform comprehensive simulation studies to compare the operating characteristics of the two designs. Our results show that, compared to the algorithm-based i3+3 design, which are characterized by ad hoc and often scientifically and logically incoherent decision rules, the mode-assisted BOIN design is not only simpler, but also statistically more rigorous with better operating characteristics, thus providing a better design choice for phase I oncology trials.
贝叶斯最优区间（BOIN）设计是一种模型辅助的I期剂量探索设计，用于寻找最大耐受剂量（MTD）。BOIN设计的标志是其简洁的决策规则——通过简单地将当前剂量下观察到的剂量限制毒性（DLT）率与一对最优剂量递增和递减边界进行比较，来做出剂量递增和递减的决策。3+3区间（i3+3）设计是最近提出的一种基于算法的剂量探索设计，它基于类似的决策规则并做了一些修改。这两种设计外观上的相似性在从业者中造成了混淆。在本文中，我们通过阐明i3+3设计与BOIN设计的联系来揭开i3+3设计的神秘面纱，并比较它们的异同以及优缺点。我们进行了全面的模拟研究来比较这两种设计的操作特性。我们的结果表明，与基于算法的i3+3设计（其特点是决策规则临时制定且往往在科学和逻辑上不连贯）相比，模型辅助的BOIN设计不仅更简单，而且在统计上更严谨，操作特性更好，从而为I期肿瘤试验提供了更好的设计选择。
