Biostatistics and Research Decision Sciences, MRL, Merck & Co., Inc., Kenilworth, New Jersey, USA.
Pharm Stat. 2020 Sep;19(5):498-517. doi: 10.1002/pst.2009. Epub 2020 Mar 14.
Test-then-pool is a simple statistical method that borrows historical information to improve efficiency of the drug development process. The original test-then-pool method examines the difference between the historical and current information and then pools the information if there is no significant difference. One drawback of this method is that a nonsignificant difference may not always imply consistency between the historical and current information. As a result, the original test-then-pool method is more likely to incorrectly borrow information from the historical control when the current trial has a small sample size. Statistically, it is more natural to use an equivalence test for examining the consistency. This manuscript develops an equivalence-based test-then-pool method for a continuous endpoint, explains the relationship between the two test-then-pool methods, explores the choice of an equivalence margin through the overlap probability, and proposes an adjustment to the nominal testing level for controlling type I error under the true consistency scenario. Furthermore, the analytical forms of the type I error and power for the two test-then-pool methods are derived, and practical considerations for using them are presented.
检验-合并是一种简单的统计学方法，通过借鉴历史信息来提高药物研发过程的效率。原始的检验-合并方法检查历史信息和当前信息之间的差异，如果没有显著差异，则对信息进行合并。该方法的一个缺点是，无显著差异并不总是意味着历史信息和当前信息之间的一致性。因此，当当前试验的样本量较小时，原始的检验-合并方法更有可能错误地从历史对照中借用信息。从统计学的角度来看，使用等效性检验来检查一致性更为自然。本文为连续终点开发了一种基于等效性的检验-合并方法，解释了两种检验-合并方法之间的关系，通过重叠概率探索等效性边界的选择，并提出了一种在真实一致性情况下控制Ⅰ型错误的名义检验水平的调整。此外，推导了两种检验-合并方法的Ⅰ型错误和功效的解析形式，并提出了使用它们的实际考虑因素。
