小样本校正方法在分析平行集群随机试验时控制I型错误的性能:对模拟研究的系统审查
K Hemming1, J Thompson1, C Kristunas2
1Applied Health Research, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, UK.
Journal of clinical epidemiology
|June 1, 2025
概括
对于集群随机试验 (CRT) 的小样本纠正可以在少数集群中保持I型错误. 然而,需要超过40个集群来确保CRT在所有情况下的I型名义错误.
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
- 统计推理 统计推理
背景情况:
- 集群随机试验 (CRT) 通常少于50个集群.
- 对于CRT的分析方法通常假定样本大小.
- 这种假设可能不成立,可能会影响治疗效果估计的可靠性.
研究的目的:
- 审查模拟研究文献关于平行CRT的小样本校正.
- 在小样本条件下评估各种分析方法的性能.
- 为了确定保留标称I型错误率的纠正.
主要方法:
- 在Ovid Medline和Web of Science系统搜索模拟研究,截至2024年8月30日.
- 包括评估二进制和连续结果的研究,使用通用线性混合模型,通用估计方程或集群级分析.
- 独立的重复全文选和数据抽象.
主要成果:
- 对于连续结果,集群级别分析,带有萨特斯威特校正的线性混合模型,以及带有费和格劳巴德校正的GEE通常保留最少六个集群的I型错误.
- 对于二进制结果,未加权/反变量加权的集群级别分析和GLMM与内部纠正之间可以在10个集群中实现标称I型错误,但在某些设置中可能是反保守或保守的.
- 需要超过40个集群来保证所有评估设置中的标称I型错误.
结论:
- 在并行CRT中进行小样本校正的性能复杂且取决于上下文.
- 虽然一些纠正只适用于很少的集群,但对I型错误的强有力的控制需要更多的集群.
- 可能需要进一步的研究来完善在小样本CRT中针对特定场景的校正.
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