Combination of several anti-cancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I-II dose finding designs for dual-agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two-stage Bayesian adaptive design that accommodates a change of patient population in-between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random-effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The inclusion of nonexchangeability assumption further enables that the stage-specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters \textit{a priori}

翻译：多种抗癌药物的联合使用通常被认为能增强药物活性。受一项真实临床试验启发，本文针对双药剂组合的I-II期剂量探索设计展开研究，其主要目标之一是同时表征毒性和疗效特征。我们提出了一种两阶段贝叶斯适应性设计，该设计能够适应中间阶段患者群体的变化。在第一阶段，我们基于过量控制剂量递增（EWOC）原则估算最大耐受剂量组合。随后在第二阶段，针对新的但相关的患者群体，寻找最具疗效的剂量组合。我们采用稳健的贝叶斯分层随机效应模型，允许在阶段间共享疗效信息，假设相关参数具有可交换性或不可交换性。在可交换性假设下，为主效应参数指定随机效应分布，以捕捉阶段间差异的不确定性。不可交换性假设的加入进一步使得阶段特异性疗效参数拥有各自先验分布。通过广泛的模拟研究评估了所提方法。结果表明，在参数先验可交换性的保守假设下，该方法对疗效评估的操作特征有普遍改善。