用于在线错误控制的ADDIS-图表与平台试验的应用
Lasse Fischer1, Marta Bofill Roig2, Werner Brannath1
1Competence Center for Clinical Trials Bremen, University of Bremen, Bremen, Germany.
Biometrical journal. Biometrische Zeitschrift
|September 28, 2025
概括
我们介绍了ADDIS-Graphs,这是一种灵活的新方法,用于在平台试验等研究中的在线错误控制. 这种方法提高了统计能力和适应性,可以有效地测试多个假设.
科学领域:
- 统计方法学的统计方法.
- 生物统计学 生物统计学
- 临床试验的设计
背景情况:
- 在线错误控制对于连续的假设测试至关重要,管理家族错误率 (FWER) 或错误发现率 (FDR).
- 现有的方法往往是刚性的,是为大规模研究而设计的,对于较小的,适应性试验 (如平台试验) 缺乏灵活性.
- 平台试验面临着独特的挑战,包括由于共享控制臂和预先规定的显著性水平的需要而导致的依赖p值.
研究的目的:
- 提出一种新的,灵活的,可解释的图形方法,用于在线错误控制在连续假设测试.
- 在较小的研究和平台试验环境中解决现有方法的局限性.
- 提高假设测试的统计能力和适应性,同时保持严格的错误控制.
主要方法:
- 引入适应性丢弃图表 (ADDIS图表) 来控制家族错误率 (FWER).
- 开发ADDIS图表的扩展,包括关于p值联合分布的信息.
- 创建一个版本的ADDIS-图表用于错误发现率 (FDR) 控制.
主要成果:
- 阿迪斯图表显示了与最先进的方法相比,可证明的均改进.
- ADDIS-Graphs的图形结构允许完美适应平台试验设置.
- 扩展增强了该方法利用依赖p值和控制FDR的信息的能力.
结论:
- ADDIS-Graphs提供了一种强大而灵活的解决方案,用于在线错误控制,用于测试顺序假设.
- 提出的方法特别适用于适应性平台试验和类似的设置.
- 这些进步在复杂的研究设计中提高了统计效率和可解释性.
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