联合用药癌症临床试验的贝叶斯无缝分期I-II试验设计（两阶段）
A Bayesian seamless phase I-II trial design with two stages for cancer clinical trials with drug combinations
摘要：
在过去的几年中，药物组合在临床试验中的使用越来越普遍，因为通过组合药物可以获得更有利的治疗反应。在I期临床试验中，大多数现有方法推荐一种独特的剂量组合为“最佳”，这可能导致随后的II期临床试验失败，因为其他剂量组合在相同毒性水平下可能具有更高的治疗疗效。我们特别感兴趣的是需要等待几个治疗周期才能观察疗效结局的情况，以及I期和II期患者人群在治疗疗效方面不同的情况。在这些情况下，通常的做法是实施两阶段设计，其中在第一阶段选择一组最大耐受剂量组合，然后在第二阶段研究治疗疗效。在这篇文章中，我们提出了一个新的两阶段设计的早期阶段的临床试验与药物组合。在第一阶段，使用二元毒性数据来指导剂量递增并设定最大耐受剂量组合。在第二阶段，我们采用第一阶段推荐的最大耐受剂量组合，该组合在整个第二阶段保持固定，并通过自适应随机化，我们将后续队列的患者分配到可能具有较高后中位数至进展时间的剂量组合中。该方法进行了广泛的模拟评估，并与一个真正的审判举例说明。
英文摘要：
The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as "optimal", which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I and II population of patients are different with respect to treatment efficacy. Under these circumstances, it is common practice to implement two-stage designs where a set of maximum tolerated dose combinations is selected in a first stage, and then studied in a second stage for treatment efficacy. In this article we present a new two-stage design for early phase clinical trials with drug combinations. In the first stage, binary toxicity data is used to guide the dose escalation and set the maximum tolerated dose combinations. In the second stage, we take the set of maximum tolerated dose combinations recommended from the first stage, which remains fixed along the entire second stage, and through adaptive randomization, we allocate subsequent cohorts of patients in dose combinations that are likely to have high posterior median time to progression. The methodology is assessed with extensive simulations and exemplified with a real trial.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
