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来自分层集群随机试验的连续数据分析方法的性能 - 一个模拟研究
Sayem Borhan1,2, Jinhui Ma1, Alexandra Papaioannou3,4
1Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Contemporary clinical trials communications
|July 3, 2023
概括
这项研究比较了分层集群随机试验 (CRT) 的分析方法. 与其他方法相比,元回归显示效率较低,I型错误率更高,特别是较少的集群.
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
- 流行病学 流行病学
背景情况:
- 分层集群随机试验 (CRT) 越来越多地被用于研究.
- 这种设计涉及在随机分配到治疗组之前将集群分组为分层.
- 从分层CRT中准确分析连续数据至关重要.
研究的目的:
- 评估用于分析分层CRT连续数据的常用统计方法的性能.
- 为了比较混合效应,一般化估计方程 (GEE),集群级 (CL) 线性回归和元回归方法.
- 根据I型错误率,功率,精度 (RMSE) 和置信区间特征来评估性能.
主要方法:
- 使用分层CRT设计进行了模拟研究,其中有一个分层变量和两个层.
- 模拟改变了集群的数量,集群大小,集群内部相关系数 (ICC) 和效应大小.
- 四种分析方法进行了比较:混合效应,GEE,CL线性回归和元回归.
主要成果:
- 通用估计方程 (GEE) 和元回归方法在少量集群中表现出高的I型错误率 (>10%).
- 所有方法都表现出类似的准确性 (RMSE) 和95%的置信区间 (CI) 宽度,除了元回归,特别是较少的集群.
- 在所有方法中,随着给定样本大小的集群内相关系数 (ICC) 的增加,实证功率下降.
结论:
- 发现元回归是分析分层CRT连续数据的最不有效方法.
- 分析方法的选择会影响结果的可靠性,特别是关于小集群设置中的I型错误率.
- 可能需要进一步的研究来完善分层CRT数据分析的方法.
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