Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
Stat Methods Med Res. 2022 Dec;31(12):2470-2485. doi: 10.1177/09622802221129051. Epub 2022 Oct 3.
When testing multiple hypotheses, a suitable error rate should be controlled even in exploratory trials. Conventional methods to control the False Discovery Rate assume that all -values are available at the time point of test decision. In platform trials, however, treatment arms enter and leave the trial at different times during its conduct. Therefore, the actual number of treatments and hypothesis tests is not fixed in advance and hypotheses are not tested at once, but sequentially. Recently, for such a setting the concept of online control of the False Discovery Rate was introduced. We propose several heuristic variations of the LOND procedure (significance Levels based On Number of Discoveries) that incorporate interim analyses for platform trials, and study their online False Discovery Rate via simulations. To adjust for the interim looks spending functions are applied with O'Brien-Fleming or Pocock type group-sequential boundaries. The power depends on the prior distribution of effect sizes, for example, whether true alternatives are uniformly distributed over time or not. We consider the choice of design parameters for the LOND procedure to maximize the overall power and investigate the impact on the False Discovery Rate by including both concurrent and non-concurrent control data.
在测试多个假设时，即使是探索性试验也应该控制合适的错误率。控制假发现率的传统方法假设在测试决策时所有 - 值都可用。然而，在平台试验中，治疗臂在试验进行过程中以不同的时间进入和离开试验。因此，实际的治疗方法数量和假设检验不是预先固定的，也不是一次性进行的，而是顺序进行的。最近，针对这种情况，引入了假发现率的在线控制概念。我们提出了几种基于发现次数的显著性水平（LOND）程序的启发式变体，这些变体将平台试验的中期分析纳入其中，并通过模拟研究其在线假发现率。为了调整中期观察的花费，使用 O'Brien-Fleming 或 Pocock 类型的组序贯边界来应用函数。功效取决于效应大小的先验分布，例如，真实的替代方案是否随时间均匀分布。我们考虑选择 LOND 程序的设计参数来最大化整体功效，并研究通过包含同时和非同时控制数据对假发现率的影响。
