Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not all necessarily pre-specified. Online error control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online testing framework, hypotheses are tested in a sequential manner, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this paper, we describe how to apply online error control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni procedure. We also illustrate how online error control would have impacted a currently ongoing platform trial.

翻译：平台测试根据单一总协议对多个实验性处理进行评估, 在一个总协议下, 新的治疗武器会随着时间推移而添加到试验中。 在多重处理比较中, 平台测试根据一个单一的总协议对多个实验性处理进行评估。 在多重处理比较中, 总体的I型误差率有可能出现通货膨胀, 由于假设在不同时间测试, 且不一定全部是预设的, 使整个I型误差率变得复杂。 在线错误控制方法为平台测试的多重性问题提供了可能的解决方案, 在平台测试中, 预计将长期测试数量相对较多的假设。 在在线测试框架中, 假设会以顺序测试方式进行测试, 在每个时间步骤中, 分析员会决定是否在不知道未来测试的情况下拒绝目前的无效假设性假设, 并且仅仅根据以往的决定来决定。 最近开发了对错误发现率和家庭错差率进行在线控制的方法( FWER ) 。 在本文中, 我们描述了如何对平台测试程序进行在线控制, 如何影响当前的在线控制。