Correcting a Significance Test for Clustering
Correcting a Significance Test for Clustering
复制标题
纠正聚类的显着性检验
DOI：
10.3102/1076998606298040
复制
发表时间：
2007
影响因子：
2.4
通讯作者：
L. Hedges
中科院分区：
文献类型：
--
作者：
L. Hedges
在分组随机试验分析中的一个常见错误是忽略分组的影响，并将每个处理组作为一个简单的随机样本来分析数据。这通常会导致对结果的精确度和关于治疗效果的精确度和统计意义的反保守结论的夸大。本文对t统计量进行了简单的更正，如果(错误地)忽略了集群，则会计算t统计量。校正是一个乘性因子，取决于总样本大小、簇大小和组内相关性ρ。修正后的t统计量具有减少自由度的学生t分布。当ρ=0时，校正后的统计量减少为通过忽略聚类而计算的t统计量。当ρ=1时，它简化为使用聚类平均计算的t统计量。如果0<；ρ<；1，则它介于这两个极值之间，且自由度介于对应于这两个极值的自由度之间。
A common mistake in analysis of cluster randomized trials is to ignore the effect of clustering and analyze the data as if each treatment group were a simple random sample. This typically leads to an overstatement of the precision of results and anticonservative conclusions about precision and statistical significance of treatment effects. This article gives a simple correction to the t statistic that would be computed if clustering were (incorrectly) ignored. The correction is a multiplicative factor depending on the total sample size, the cluster size, and the intraclass correlation ρ. The corrected t statistic has Student’s t distribution with reduced degrees of freedom. The corrected statistic reduces to the t statistic computed by ignoring clustering when ρ = 0. It reduces to the t statistic computed using cluster means when ρ = 1. If 0 < ρ < 1, it lies between these two, and the degrees of freedom are in between those corresponding to these two extremes.