Many phase I drug combination designs have been proposed to find the maximum tolerated combination (MTC). Due to the two‐dimension nature of drug combination trials, these designs typically require complicated statistical modeling and estimation, which limit their use in practice. In this article, we propose an easy‐to‐implement Bayesian phase I combination design, called Bayesian adaptive linearization method (BALM), to simplify the dose finding for drug combination trials. BALM takes the dimension reduction approach. It selects a subset of combinations, through a procedure called linearization, to convert the two‐dimensional dose matrix into a string of combinations that are fully ordered in toxicity. As a result, existing single‐agent dose‐finding methods can be directly used to find the MTC. In case that the selected linear path does not contain the MTC, a dose‐insertion procedure is performed to add new doses whose expected toxicity rate is equal to the target toxicity rate. Our simulation studies show that the proposed BALM design performs better than competing, more complicated combination designs.

中文翻译：

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贝叶斯自适应线性化方法用于降维的一期药物组合试验

已经提出了许多I期药物组合设计以找到最大耐受组合（MTC）。由于药物组合试验的二维性质，这些设计通常需要复杂的统计建模和估计，这限制了它们在实践中的使用。在本文中，我们提出了一种易于实现的贝叶斯I期组合设计，称为贝叶斯自适应线性化方法（BALM），以简化药物组合试验的剂量发现。BALM采用降维方法。它通过称为线性化的过程选择组合的子集，以将二维剂量矩阵转换为毒性完全排序的一连串组合。因此，可以直接使用现有的单剂剂量查找方法来找到MTC。如果所选的线性路径不包含MTC，则执行剂量插入程序以添加其预期毒性率等于目标毒性率的新剂量。我们的仿真研究表明，拟议的BALM设计的性能优于竞争性，更复杂的组合设计。