Wason James, Magirr Dominic, Law Martin, Jaki Thomas
MRC Biostatistics Unit, Cambridge, UK
Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, UK.
Stat Methods Med Res. 2016 Apr;25(2):716-27. doi: 10.1177/0962280212465498. Epub 2012 Dec 12.
Multi-arm multi-stage designs can improve the efficiency of the drug-development process by evaluating multiple experimental arms against a common control within one trial. This reduces the number of patients required compared to a series of trials testing each experimental arm separately against control. By allowing for multiple stages experimental treatments can be eliminated early from the study if they are unlikely to be significantly better than control. Using the TAILoR trial as a motivating example, we explore a broad range of statistical issues related to multi-arm multi-stage trials including a comparison of different ways to power a multi-arm multi-stage trial; choosing the allocation ratio to the control group compared to other experimental arms; the consequences of adding additional experimental arms during a multi-arm multi-stage trial, and how one might control the type-I error rate when this is necessary; and modifying the stopping boundaries of a multi-arm multi-stage design to account for unknown variance in the treatment outcome. Multi-arm multi-stage trials represent a large financial investment, and so considering their design carefully is important to ensure efficiency and that they have a good chance of succeeding.
多臂多阶段设计可以通过在一次试验中针对一个共同对照评估多个试验组来提高药物研发过程的效率。与一系列分别针对对照测试每个试验组的试验相比，这减少了所需的患者数量。通过允许进行多个阶段，如果试验性治疗不太可能显著优于对照，则可以在研究早期将其排除。以TAILoR试验作为一个有启发性的例子，我们探讨了与多臂多阶段试验相关的广泛统计问题，包括为多臂多阶段试验设定检验效能的不同方法的比较；与其他试验组相比选择对照组的分配比例；在多臂多阶段试验期间增加额外试验组的后果，以及在必要时如何控制I型错误率；以及修改多臂多阶段设计的停止边界以考虑治疗结果中未知的方差。多臂多阶段试验代表着巨大的财务投资，因此仔细考虑其设计对于确保效率以及提高成功的可能性非常重要。
