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用于具有不稳定对照的非劣效性试验的单阶段、三臂自适应试验策略

Single-stage, three-arm, adaptive test strategies for non-inferiority trials with an unstable reference.

作者信息

Brannath Werner, Scharpenberg Martin, Schmidt Sylvia

机构信息

Competence Center for Clinical Trials Bremen, University of Bremen, Bremen, Germany.

出版信息

Stat Med. 2022 Nov 10;41(25):5033-5045. doi: 10.1002/sim.9552. Epub 2022 Aug 18.

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 -superiority of the experimental treatment over placebo is tested. With a properly chosen superiority margin, -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

对于仅存在不稳定对照治疗且使用安慰剂在伦理上合理的适应症,非劣效性试验推荐采用包含试验组、对照组和安慰剂组的三臂“金标准”设计。在这种设计中,需要证明对照或试验治疗的有效性。其缺点是,如果对照治疗无效,从试验中能得出的结论很少。为克服这一问题,我们研究了新颖的单阶段适应性检验策略,即仅在对照显示出足够疗效时才检验非劣效性,否则检验试验治疗相对于安慰剂的优效性。通过适当选择优效性界值,优效性间接表明非劣效性。我们针对几种决策规则优化了样本量,发现自然的、数据驱动的检验策略(即对照的疗效检验显著时检验非劣效性)会导致总体样本量和安慰剂样本量最小。我们证明,在样本量的特定约束条件下,该程序可控制家族性错误率。所有最优样本量均满足此约束。我们最后展示了如何有效考虑相关的安慰剂脱落率,并将新的检验策略应用于一个实际数据集。

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