Shao Jun, Feng Huaibao
Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States.
Contemp Clin Trials. 2007 Sep;28(5):563-71. doi: 10.1016/j.cct.2007.02.006. Epub 2007 Mar 1.
Interim analyses are often applied in clinical trials for various reasons. To assess the effect of a clinical treatment, the group sequential t-test with a fixed number of interim analyses is frequently used in clinical trials. The existing critical values used in group sequential t-tests are obtained from normal approximations of t-statistics. In practice, however, normal approximation is not accurate when some sample sizes of treatment arms in some stages are small. In this paper, instead of using normal approximation, we directly obtain the critical values via a Monte Carlo method. We list some critical values for certain sample sizes and number of interim analyses, and provide some SAS code for general situations. We also consider the sample size calculation and run some simulations to check the accuracy of our critical values. The simulation results show that our critical values yield type I error probabilities that are very close to the nominal significance level, whereas the existing critical values based on normal approximation are not accurate when some sample sizes are small across stages.
出于各种原因，期中分析常用于临床试验。为评估临床治疗效果，临床试验中常使用具有固定期中分析次数的成组序贯t检验。成组序贯t检验中使用的现有临界值是通过t统计量的正态近似获得的。然而，在实际中，当某些阶段治疗组的一些样本量较小时，正态近似并不准确。在本文中，我们不使用正态近似，而是通过蒙特卡罗方法直接获得临界值。我们列出了某些样本量和期中分析次数的一些临界值，并提供了一般情况下的一些SAS代码。我们还考虑了样本量计算，并进行了一些模拟以检验我们临界值的准确性。模拟结果表明，我们的临界值产生的I型错误概率非常接近名义显著性水平，而基于正态近似的现有临界值在各阶段一些样本量较小时并不准确。
