Sample size considerations for GEE analyses of three-level cluster randomized trials.
Sample size considerations for GEE analyses of three-level cluster randomized trials.
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DOI：
10.1111/j.1541-0420.2009.01374.x
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发表时间：
2010-12
期刊：
影响因子：
1.9
通讯作者：
Borm GF
中科院分区：
文献类型：
--
作者：
Teerenstra S;Lu B;Preisser JS;van Achterberg T;Borm GF
医疗保健中的群集随机试验可能涉及三个而不是两个水平，例如，在比较不同干预措施以提高护理质量的试验中。在这些试验中，干预措施在医疗单位（“集群”）中实施，旨在改变在该单位工作的医疗保健专业人员（“受试者”）的行为，而效果则在患者层面进行测量（“评估”）。在广义估计方程（GEE）的方法，我们推导出一个样本量公式，占两个层次的集群：集群内的主题和主题内的评价。该公式表明，样本量膨胀，相对于设计与完全独立的评价，由一个乘法项，可以表示为产品的两个方差膨胀因子，一个量化的受试者内相关性的评价受试者水平的均值方差的影响，另一个量化的受试者水平的均值之间的相关性对集群均值方差的影响。当使用相关参数的偏倚校正估计方程结合基于模型的协方差估计量或具有有限样本校正的三明治估计量分析数据时，样本量公式预测的功效水平与总共超过10个聚类的模拟功效一致。
Cluster randomized trials in health care may involve three instead of two levels, for instance, in trials where different interventions to improve quality of care are compared. In such trials, the intervention is implemented in health care units (“clusters”) and aims at changing the behavior of health care professionals working in this unit (“subjects”), while the effects are measured at the patient level (“evaluations”). Within the generalized estimating equations (GEE) approach, we derive a sample size formula that accounts for two levels of clustering: that of subjects within clusters and that of evaluations within subjects. The formula reveals that sample size is inflated, relative to a design with completely independent evaluations, by a multiplicative term that can be expressed as a product of two variance inflation factors, one that quantifies the impact of within-subject correlation of evaluations on the variance of subject-level means and the other that quantifies the impact of the correlation between subject level means on the variance of the cluster means. Power levels as predicted by the sample size formula agreed well with the simulated power for more than 10 clusters in total, when data was analyzed using bias-corrected estimating equations for the correlation parameters in combination with the model-based covariance estimator or the sandwich estimator with a finite sample correction.
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影响因子：
1.9
作者：
Lu, Bing;Preisser, John S.;Wolfson, Mark
通讯作者：
Wolfson, Mark
影响因子：
2.7
作者：
Teerenstra, Steven;Moerbeek, Mirjam;Borm, George F.
通讯作者：
Borm, George F.
影响因子：
2.7
作者：
Qaqish, BF
通讯作者：
Qaqish, BF
影响因子：
1.9
作者：
Heo, Moonseong;Leon, Andrew C.
通讯作者：
Leon, Andrew C.
DOI：
10.3102/10769986025003271
发表时间：
2000-09-01
影响因子：
2.4
作者：
Moerbeek, M;van Breukelen, GJP;Berger, MPF
通讯作者：
Berger, MPF