Grossman J, Parmar M K, Spiegelhalter D J, Freedman L S
Department of Community Medicine, University of Sydney, NSW, Australia.
Stat Med. 1994 Sep 30;13(18):1815-26. doi: 10.1002/sim.4780131804.
Group sequential methods are becoming increasingly popular for monitoring and analysing large controlled trials, especially clinical trials. They not only allow trialists to monitor the data as it accumulates, but also reduce the expected sample size. Such methods are traditionally based on preserving the overall type I error by increasing the conservatism of the hypothesis tests performed at any single analysis. Using methods which are based on hypothesis testing in this way makes point estimation and the calculation of confidence intervals difficult and controversial. We describe a class of group sequential procedures based on a single parameter which reflects initial scepticism towards unexpectedly large effects. These procedures have good expected and maximum sample sizes, and lead to natural point and interval estimates of the treatment difference. Hypothesis tests, point estimates and interval estimates calculated using this procedure are consistent with each other, and tests and estimates made at the end of the trial are consistent with interim tests and estimates. This class of sequential tests can be considered in both a traditional group sequential manner or as a Bayesian solution to the problem.
序贯分组方法在监测和分析大型对照试验（尤其是临床试验）中越来越受欢迎。它们不仅允许试验者在数据积累时进行监测，还能减少预期样本量。传统上，此类方法通过增加在任何单次分析中进行的假设检验的保守性来保持总体I型错误率。以这种基于假设检验的方式使用方法会使点估计和置信区间的计算变得困难且存在争议。我们描述了一类基于单个参数的序贯分组程序，该参数反映了对意外大效应的初始怀疑态度。这些程序具有良好的预期样本量和最大样本量，并能得出治疗差异的自然点估计和区间估计。使用该程序计算的假设检验、点估计和区间估计相互一致，且试验结束时的检验和估计与中期检验和估计一致。这类序贯检验既可以按照传统的序贯分组方式考虑，也可以作为该问题的贝叶斯解决方案。
