Abstract：
There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multistage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms.
摘要：
人们对平台试验的兴趣与日俱增，这些试验允许随着试验的进行而增加新的治疗手段，并且能够在试验中部分停止治疗，因为缺乏益处/徒劳或优势。在某些情况下,平台试验需要保证错误率得到控制。本文提出了一种多级设计，这允许以预先计划的方式在平台试验中添加额外的武器，在仍然控制家庭错误率的同时，在已知数量和时间的假设下，没有时间趋势。给出了一种方法来计算达到所需功率水平所需的样本量，我们展示了如何找到样本量的分布和预期样本量。我们专注于在最不有利的配置下的功率，即从一组治疗中找到具有临床相关效果的治疗，而其余的则具有无趣的治疗效果。提出了一个激励试验，重点是两个设置，第一个是每个主动治疗臂的设定数量的阶段，第二个是设定的总阶段数量，后来增加的治疗阶段更少。与Bonferroni相比,在三支手臂的试验中，总最大样本量的节省是适度的，<总样本量的1%。然而,在使用更多武器的试验中，节省的资金更加可观。
