对并发平衡或不平衡多次干预阶段设计的功率分析:基于模拟的方法
Yi Zhang1,2, Meng Zheng1,2, Xue-Zhi Liang1,2
1Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
BMC medical research methodology
|April 16, 2025
概括
对于并发的多次干预阶段形设计 (M-SWD),当治疗效果显著不同时,偏好不平衡的组分配,从而提高统计能力. 增加集群数量和考虑相关性参数是有效的M-SWD试验的关键.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
背景情况:
- 多干预阶段设计 (M-SWD) 是一种广泛采用的集群随机试验设计.
- 对并发平衡和不平衡M-SWD的功率分析对于高效的试验规划至关重要.
研究的目的:
- 对并发平衡和不平衡的M-SWD进行功率分析.
- 评估设计和相关性参数对统计功率的影响.
主要方法:
- 基于模拟的功率分析,使用横截面或闭合队列数据.
- 检查设计参数:集群大小和集群数量.
- 对相关性参数的评估:总随机效应方差 (TRE),集群自相关系数 (CAC) 和个体自相关系数 (IAC).
主要成果:
- 增加集群数量可以提高固定总样本大小的统计能力.
- 同时不平衡的M-SWD在处理效果不相似时提供样本大小节省,最佳分配比率高达4:1.
- 随着TRE的减少和CAC和IAC的增加,统计能力会增加,自相关性在较大的值上具有更明显的效果.
结论:
- 同时不平衡的M-SWD在处理效果实质上不同时 (分配比率≤4:1) 优先于平衡的设计.
- 对于平衡和不平衡的M-SWD,建议使用大量较小的集群.
- 在M-SWD变体的试验设计过程中,对相关性参数估计的仔细考虑至关重要.
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