There is a growing interest in the implementation of platform trials, which provide the flexibility to incorporate new treatment arms during the trial and the ability to halt treatments early based on lack of benefit or observed superiority. In such trials, it can be important to ensure that error rates are controlled. This paper introduces a multi-stage design that enables the addition of new treatment arms, at any point, in a pre-planned manner within a platform trial, while still maintaining control over the family-wise error rate. This paper focuses on finding the required sample size to achieve a desired level of statistical power when treatments are continued to be tested even after a superior treatment has already been found. This may be of interest if there are other sponsors treatments which are also superior to the current control or multiple doses being tested. The calculations to determine the expected sample size is given. A motivating trial is presented in which the sample size of different configurations is studied. Additionally the approach is compared to running multiple separate trials and it is shown that in many scenarios if family wise error rate control is needed there may not be benefit in using a platform trial when comparing the sample size of the trial.

翻译：平台试验的实施正受到日益关注，其优势在于试验过程中可灵活加入新治疗组，并能基于疗效不足或优势显现提前终止治疗。此类试验需确保错误率得到有效控制。本文介绍一种多阶段设计方法，可在平台试验中按预设计划在任意时间点新增治疗组，同时维持对族系错误率的控制。研究重点在于：当已发现优效治疗后仍需继续检验其他治疗组时，如何确定达到预期统计功效所需的样本量。该需求可能源于存在其他优于当前对照的赞助方治疗方案，或需检验多个剂量组。本文给出期望样本量的计算公式，并通过一项动机性试验展示不同配置下的样本量研究。此外，将本方法与并行开展多个独立试验进行比较，结果表明：在需要控制族系错误率的多数场景下，就样本量效率而言，采用平台试验未必具有优势。