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超越Bonferroni:在不成比例的危险下,对时间与事件数据进行新的多重对比测试
Ina Dormuth1, Carolin Herrmann2, Frank Konietschke3
1TU Dortmund University, Dortmund, Germany. ina.dormuth@tu-dortmund.de.
Lifetime data analysis
|January 14, 2026
概括
新的临床试验试验为分析时间到事件数据提供了更好的能力,特别是与不成比例的危险. 这些方法控制了家族错误率 (FWER),而不需要额外的p值调整.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 生存分析的分析.
背景情况:
- 在临床试验中比较多个组需要识别特定的差异,需要测试多个假设.
- 控制家族错误率 (FWER) 是至关重要的,通常通过诸如对时间到事件数据的Bonferroni-corrected log-rank测试等方法实现.
- 现有的方法,如邦费罗尼校正的日志排名测试有局限性,包括在违反比例危险假设的情况下降低功率和降低整体功率,特别是在依赖测试统计数据的情况下.
研究的目的:
- 开发新的统计测试,以比较临床试验中的多个组与时间到事件数据.
- 解决传统FWER控制方法的功率限制,特别是在不成比例的危险的情况下.
- 引入新的测试,通过设计控制FWER,并可能提供更高的功率.
主要方法:
- 提出了基于组合加权日志等级测试的两个新测试.
- 开发了使用加权日志等级测试的多重对比测试.
- 将因数设计的CASANOVA测试方法扩展为新的多重对比测试.
- 进行了广泛的蒙特卡洛模拟,以评估在比例和非比例危险下测试性能.
- 将新方法和现有方法应用于现实世界的临床试验数据集.
主要成果:
- 拟议的多重对比测试和基于CASANOVA的测试有效控制了FWER.
- 这两种新方法在各种场景中都显示出合理的统计能力,包括比例和非比例风险.
- 在某些不成比例的危险设置中,与调整的传统方法相比,新方法在功率方面表现优越.
- 基于CASANOVA的方法消除了额外的p值校正的需要,简化了分析.
结论:
- 新开发的测试为分析多组临床试验中的时间到事件数据提供了更强大的替代方案.
- 这些方法提供了FWER强大的控制和更好的功率,特别是在具有不成比例危险的复杂场景中.
- 提出的方法代表了临床试验分析的统计方法学的重大进步,提高了检测真正治疗效果的能力.
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