论文标题
群集随机和阶梯楔试验的功率和样本量：比较通过应用设计效果或通过直接估算GLMM获得的估计值
Power and sample size for cluster randomized and stepped wedge trials: Comparing estimates obtained by applying design effects or by direct estimation in GLMM
论文作者
论文摘要
当观察是独立的时，公式和软件很容易就可以计划和设计适当的大小和功率来检测重要关联的研究。当观测值相关或聚集时，从标准软件获得的结果需要调整。本教程使用了两种方法，使用了为独立数据和聚类数据的各种设计的示例进行比较。 一种方法使用在观测值之间假定独立性的软件获得初始估计，然后使用设计效果（DE）调整这些估计值，也称为方差通胀因子（VIF）。第二种方法使用通用的线性混合模型（GLMM）生成估计值，该模型直接解释了聚类和相关模式。 这两种方法通常会产生相似的估计，因此相互验证。对于某些集群设计，功率估计的微小差异强调了以手段以及预期的方差和协方差来指定替代假设的重要性。两种功率估计方法都对集群结果之间的独立性或相关性的结构或模式的假设敏感。
When observations are independent, formulae and software are readily available to plan and design studies of appropriate size and power to detect important associations. When observations are correlated or clustered, results obtained from the standard software require adjustment. This tutorial compares two approaches, using examples that illustrate various designs for both independent and clustered data. One approach obtains initial estimates using software that assume independence among observations, then adjusts these estimates using a design effect (DE), also called a variance inflation factor (VIF). A second approach generates estimates using generalized linear mixed models (GLMM) that account directly for patterns of clustering and correlation. The two approaches generally produce similar estimates and so validate one another. For certain clustered designs, small differences in power estimates emphasize the importance of specifying an alternative hypothesis in terms of means but also in terms of expected variances and covariances. Both approaches to power estimation are sensitive to assumptions concerning the structure or pattern of independence or correlation among clustered outcomes.
