In phase I dose escalation studies for dual-agent combinations, at least one drug often has an established monotherapy dose. Consequently, substantial prior clinical safety data often exist for one or more monotherapies, allowing the study to focus on a subset of selected dose combinations rather than exhaustively evaluating all possible dose combinations for two agents. The Bayesian Optimal Interval (BOIN) design framework is widely recognized for its robust performance and ease of implementation; however, the BOIN for combination design, abbreviated as BOIN-C in this paper, was originally developed to evaluate full combinations and may not be directly applicable for the subset of selected combinations. In this paper, we propose three extensions to the BOIN-C design to address scenarios involving selected dose combinations: (a) BOIN-CS: a generalized BOIN-C design to accommodate any subset of dose combinations. (b) BOIN-CE: Exploration of new off-diagonal dose combinations when de-escalating. This option provides additional opportunities to treat patients with dose combinations that have not been administered. (c) BOIN-CB: Bayesian logistic regression model (BLRM)-guided BOIN design, which uses the BLRM model to break the tie when two dose combinations have an equal posterior probability of being selected. This can be useful when the dose-toxicity relationship is expected to be reasonably aligned with a logistic relationship. These study design options are motivated by practical considerations, and their operating characteristics are evaluated through extensive simulations under various scenarios, demonstrating satisfactory performance.

翻译：在双药联合的I期剂量递增研究中，至少一种药物通常具有确定的单药治疗剂量。因此，往往存在一种或多种单药治疗的大量既往临床安全性数据，使得研究可聚焦于部分选择性剂量组合，而非对所有可能的两药组合进行穷举性评估。贝叶斯最优区间（BOIN）设计框架因其稳健的性能和易于实施而广受认可；然而，本文简称为BOIN-C的联合BOIN设计最初是为评估全剂量组合而开发的，可能无法直接适用于选择性剂量组合的子集。本文针对选择性剂量组合场景，提出BOIN-C设计的三种扩展方案：(a) BOIN-CS：泛化的BOIN-C设计，可适用于任意剂量组合子集；(b) BOIN-CE：在剂量递减时探索新的非对角线剂量组合。该选项为患者提供了接受未使用过的剂量组合治疗的额外机会；(c) BOIN-CB：贝叶斯逻辑回归模型（BLRM）引导的BOIN设计，当两个剂量组合被选中的后验概率相等时，利用BLRM模型打破平局。当预期剂量-毒性关系与逻辑关系合理吻合时，该方案尤为有用。这些研究设计选项源于实践考量，通过不同场景下的广泛模拟评估了其操作特征，展示了满意的性能。