测试一个主要和两个次要终点在一个两个阶段的组序列试验与扩展
Ajit C Tamhane1, Dong Xi2, Cyrus R Mehta3
1Northwestern University, Evanston, Illinois, USA.
Statistics in medicine
|January 24, 2025
概括
这项研究开发了强大的统计方法来测试在临床试验中主要终点显著之后的二次终点. 正常理论测试通过考虑终点相关性和守门效应,提供了比基于p值的方法更高的功率.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
- 统计推理 统计推理
背景情况:
- 组序列程序对于适应性临床试验设计至关重要,允许早期停止有效性或徒劳性.
- 根据主要终点显著性的条件测试多个次要终点需要仔细的统计控制来保持I型错误率.
- 现有的基于p值的方法 (Holm,Hochberg) 很简单,但由于忽视了终点相关性和守门效应,可能缺乏功率.
研究的目的:
- 开发用于测试多个次要终点的正常理论类比,在两阶段组序列试验中进行测试.
- 将主要终点的守门效应和终点之间的相关性纳入统计测试程序.
- 将拟议的正常理论程序的功率和I型错误率与现有的基于p值的方法进行比较.
主要方法:
- 开发基于正常理论的封闭程序,用于测试多个次要假设.
- 使用最不有利的相关性配置确定正常理论边界,消除了对相关性事先知识的需要.
- 在正常理论和基于p值的程序之间比较二次权力,包括对信息时间不平等的灵敏度分析.
主要成果:
- 与基于p值的霍尔姆和霍赫伯格程序相比,正常理论类比显示出更高的统计能力.
- 拟议的正常理论方法有效地考虑了守门效应和终点相关性,从而提高了功率.
- 正常理论程序在两个次要终点或阶段之外是计算密集的,而基于p值的方法仍然适用.
结论:
- 基于正常理论的程序为在特定的临床试验环境中测试多个次要终点提供了更强大的方法.
- 当无法获得准确的相关信息时,这些方法提供了一个有价值的替代方案,依赖于最不有利的配置.
- 该研究强调了在设计具有多个次要终点的组序列试验时,功率和计算复杂性之间的权衡.
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