Abstract：
For indications where only unstable reference treatments are available and use of placebo is ethically justified, three-arm \"gold standard\" designs with an experimental, reference and placebo arm are recommended for non-inferiority trials. In such designs, the demonstration of efficacy of the reference or experimental treatment is a requirement. They have the disadvantage that only little can be concluded from the trial if the reference fails to be efficacious. To overcome this, we investigate novel single-stage, adaptive test strategies where non-inferiority is tested only if the reference shows sufficient efficacy and otherwise δ $$ \\delta $$ -superiority of the experimental treatment over placebo is tested. With a properly chosen superiority margin, δ $$ \\delta $$ -superiority indirectly shows non-inferiority. We optimize the sample size for several decision rules and find that the natural, data driven test strategy, which tests non-inferiority if the reference\'s efficacy test is significant, leads to the smallest overall and placebo sample sizes. We proof that under specific constraints on the sample sizes, this procedure controls the family-wise error rate. All optimal sample sizes are found to meet this constraint. We finally show how to account for a relevant placebo drop-out rate in an efficient way and apply the new test strategy to a real life data set.
摘要：
对于只有不稳定的参考治疗可用且使用安慰剂在伦理上是合理的适应症，三臂“黄金标准”设计具有实验性，非劣效性试验推荐参考组和安慰剂组.在这样的设计中,参考或实验治疗的疗效证明是必需的。它们的缺点是，如果参考不能有效，则只能从试验中得出很少的结论。为了克服这一点，我们研究新颖的单阶段，适应性测试策略，其中仅在参考显示出足够的功效时才测试非劣效性，否则测试实验治疗优于安慰剂的δ$$\\delta$$-优越性。有了适当选择的优势，δ$$\\delta$$-优势间接显示非劣效性。我们优化了几个决策规则的样本量，发现自然，数据驱动的测试策略，如果参考的功效测试显著，则测试非劣效性，导致最小的总体和安慰剂样本量。我们证明，在样本量的特定限制下，该程序控制家庭错误率。发现所有最佳样本量都满足此约束。最后，我们展示了如何以有效的方式解释相关的安慰剂辍学率，并将新的测试策略应用于现实生活中的数据集。
