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Abstract：
BACKGROUND: Multi-arm multi-stage (MAMS) randomised trial designs have been proposed to evaluate multiple research questions in the confirmatory setting. In designs with several interventions, such as the 8-arm 3-stage ROSSINI-2 trial for preventing surgical wound infection, there are likely to be strict limits on the number of individuals that can be recruited or the funds available to support the protocol. These limitations may mean that not all research treatments can continue to accrue the required sample size for the definitive analysis of the primary outcome measure at the final stage. In these cases, an additional treatment selection rule can be applied at the early stages of the trial to restrict the maximum number of research arms that can progress to the subsequent stage(s). This article provides guidelines on how to implement treatment selection within the MAMS framework. It explores the impact of treatment selection rules, interim lack-of-benefit stopping boundaries and the timing of treatment selection on the operating characteristics of the MAMS selection design.
METHODS: We outline the steps to design a MAMS selection trial. Extensive simulation studies are used to explore the maximum/expected sample sizes, familywise type I error rate (FWER), and overall power of the design under both binding and non-binding interim stopping boundaries for lack-of-benefit.
RESULTS: Pre-specification of a treatment selection rule reduces the maximum sample size by approximately 25% in our simulations. The familywise type I error rate of a MAMS selection design is smaller than that of the standard MAMS design with similar design specifications without the additional treatment selection rule. In designs with strict selection rules - for example, when only one research arm is selected from 7 arms - the final stage significance levels can be relaxed for the primary analyses to ensure that the overall type I error for the trial is not underspent. When conducting treatment selection from several treatment arms, it is important to select a large enough subset of research arms (that is, more than one research arm) at early stages to maintain the overall power at the pre-specified level.
CONCLUSIONS: Multi-arm multi-stage selection designs gain efficiency over the standard MAMS design by reducing the overall sample size. Diligent pre-specification of the treatment selection rule, final stage significance level and interim stopping boundaries for lack-of-benefit are key to controlling the operating characteristics of a MAMS selection design. We provide guidance on these design features to ensure control of the operating characteristics.
METHODS: We outline the steps to design a MAMS selection trial. Extensive simulation studies are used to explore the maximum/expected sample sizes, familywise type I error rate (FWER), and overall power of the design under both binding and non-binding interim stopping boundaries for lack-of-benefit.
RESULTS: Pre-specification of a treatment selection rule reduces the maximum sample size by approximately 25% in our simulations. The familywise type I error rate of a MAMS selection design is smaller than that of the standard MAMS design with similar design specifications without the additional treatment selection rule. In designs with strict selection rules - for example, when only one research arm is selected from 7 arms - the final stage significance levels can be relaxed for the primary analyses to ensure that the overall type I error for the trial is not underspent. When conducting treatment selection from several treatment arms, it is important to select a large enough subset of research arms (that is, more than one research arm) at early stages to maintain the overall power at the pre-specified level.
CONCLUSIONS: Multi-arm multi-stage selection designs gain efficiency over the standard MAMS design by reducing the overall sample size. Diligent pre-specification of the treatment selection rule, final stage significance level and interim stopping boundaries for lack-of-benefit are key to controlling the operating characteristics of a MAMS selection design. We provide guidance on these design features to ensure control of the operating characteristics.
摘要：
背景：已提出多臂多阶段（MAMS）随机试验设计来评估验证性环境中的多个研究问题。在有几种干预措施的设计中，例如预防手术伤口感染的8臂3阶段ROSSINI-2试验，可能会严格限制可招募的人数或可用于支持该协议的资金。这些限制可能意味着并非所有的研究治疗都可以继续积累所需的样本量，以便在最后阶段对主要结果指标进行最终分析。在这些情况下,可以在试验的早期阶段应用额外的治疗选择规则,以限制可以进展到后续阶段的研究组的最大数量.本文提供了有关如何在MAMS框架内实施治疗选择的指南。它探讨了治疗选择规则的影响，根据MAMS选择设计的操作特性，临时缺乏收益的停止边界和治疗选择的时机。
方法：我们概述了设计MAMS选择试验的步骤。广泛的模拟研究用于探索最大/预期样本量，家庭I型错误率(FWER)，以及在具有约束力和不具有约束力的临时停止边界下设计的整体权力，以避免缺乏利益。
结果：在我们的模拟中，预先指定治疗选择规则可将最大样本量减少约25％。MAMS选择设计的家族I型错误率小于具有相似设计规范而没有附加治疗选择规则的标准MAMS设计的家族I型错误率。在具有严格选择规则的设计中-例如，当从7个研究方中只选择一个研究方时,可以放宽主要分析的最终阶段显著性水平,以确保试验的整体I型错误没有被低估.当从几个治疗臂中进行治疗选择时，重要的是选择一个足够大的研究分支子集(也就是说，多个研究部门)在早期阶段将整体力量保持在预先指定的水平。
结论：多臂多级选择设计通过减少总体样本量而获得了优于标准MAMS设计的效率。处理选择规则的预先规范,最终阶段显著性水平和缺乏效益的临时停止边界是控制MAMS选择设计的运行特性的关键。我们提供有关这些设计功能的指导，以确保对操作特性的控制。
方法：我们概述了设计MAMS选择试验的步骤。广泛的模拟研究用于探索最大/预期样本量，家庭I型错误率(FWER)，以及在具有约束力和不具有约束力的临时停止边界下设计的整体权力，以避免缺乏利益。
结果：在我们的模拟中，预先指定治疗选择规则可将最大样本量减少约25％。MAMS选择设计的家族I型错误率小于具有相似设计规范而没有附加治疗选择规则的标准MAMS设计的家族I型错误率。在具有严格选择规则的设计中-例如，当从7个研究方中只选择一个研究方时,可以放宽主要分析的最终阶段显著性水平,以确保试验的整体I型错误没有被低估.当从几个治疗臂中进行治疗选择时，重要的是选择一个足够大的研究分支子集(也就是说，多个研究部门)在早期阶段将整体力量保持在预先指定的水平。
结论：多臂多级选择设计通过减少总体样本量而获得了优于标准MAMS设计的效率。处理选择规则的预先规范,最终阶段显著性水平和缺乏效益的临时停止边界是控制MAMS选择设计的运行特性的关键。我们提供有关这些设计功能的指导，以确保对操作特性的控制。
