Power for T-test comparisons of unbalanced cluster exposure studies.
Power for T-test comparisons of unbalanced cluster exposure studies.
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不平衡集群暴露研究的 T 检验比较功效。
DOI：
10.1093/jurban/79.2.278
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
Hoover,DonaldR
中科院分区：
文献类型：
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作者：
Hoover,DonaldR
在卫生服务和流行病学研究中，对在不平衡聚类中抽样的个人进行研究已经变得很常见，但目前用于功效/样本量估计和最佳设计的工具有限。本文提出并举例说明了在不平衡研究中，当每个暴露组中的组数不等和/或每个组中的受试者数不等时，在组水平上暴露对连续结果影响的检验比较的功效估计公式，这些功效公式的迭代应用可获得所需的最小样本量和/或最小可检测差异。实现这些算法的SAS子程序在附录中给出。当可行时，通过在每个组中具有相同数量的簇kA = kB（与每个组中的簇的数量无关）和每个组中相同的受试者总数nAkA =nBkB来优化功效。每个聚类的受试者数量的成本效益上限可能约为（5/ρ）-5或更小，其中ρ是组内相关性。这里提出的简单聚类设计的方法可以扩展到涉及复杂分层加权聚类样本的某些设置。
Studies of individuals sampled in unbalanced clusters have become common in health services and epidemiological research, but available tools for power/sample size estimation and optimal design are currently limited. This paper presents and illustrates power estimation formulas fort-test comparisons of effect of an exposure at the cluster level on continuous outcomes in unbalanced studies with unequal numbers of clusters and/or unequal numbers of subjects per cluster in each exposure arm. Iterative application of these power formulas obtains minimal sample size needed and/or minimal detectable difference. SAS subroutines to implement these algorithms are given in the Appendices. When feasible, power is optimized by having the same number of clusters in each armkA=kBand (irrespective of numbers of clusters in each arm) the same total number of subjects in each armnAkA=nBkB. Cost beneficial upper limits for numbers of subjects per cluster may be approximately (5/ρ) −5 or less where ρ is the intraclass correlation. The methods presented here for simple cluster designs may be extended to some settings involving complex hierarchical weighted cluster samples.