Department of Methodology and Statistics, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.
J Clin Epidemiol. 2012 Nov;65(11):1212-8. doi: 10.1016/j.jclinepi.2012.06.002.
Simple guidelines for calculating efficient sample sizes in cluster randomized trials with unknown intraclass correlation (ICC) and varying cluster sizes.
A simple equation is given for the optimal number of clusters and sample size per cluster. Here, optimal means maximizing power for a given budget or minimizing total cost for a given power. The problems of cluster size variation and specification of the ICC of the outcome are solved in a simple yet efficient way.
The optimal number of clusters goes up, and the optimal sample size per cluster goes down as the ICC goes up or as the cluster-to-person cost ratio goes down. The available budget, desired power, and effect size only affect the number of clusters and not the sample size per cluster, which is between 7 and 70 for a wide range of cost ratios and ICCs. Power loss because of cluster size variation is compensated by sampling 10% more clusters. The optimal design for the ICC halfway the range of realistic ICC values is a good choice for the first stage of a two-stage design. The second stage is needed only if the first stage shows the ICC to be higher than assumed.
Efficient sample sizes for cluster randomized trials are easily computed, provided the cost per cluster and cost per person are specified.
针对具有未知组内相关系数（ICC）和变化的簇大小的聚类随机试验，提供计算有效样本量的简单准则。
给出了最优簇数和每个簇样本量的简单方程。这里，最优意味着在给定预算下最大化功效，或在给定功效下最小化总成本。以简单而有效的方式解决了簇大小变化和结果 ICC 规范的问题。
随着 ICC 的增加或簇与个体成本比的降低，最优簇数增加，每个簇的最优样本量减少。可用预算、期望功效和效应大小仅影响簇数，而不影响每个簇的样本量，对于广泛的成本比和 ICC 范围，样本量在 7 到 70 之间。由于簇大小变化导致的功效损失可以通过多采样 10%的簇来补偿。在现实 ICC 值范围内 ICC 的中间值的最优设计是两阶段设计第一阶段的一个不错选择。仅当第一阶段显示 ICC 高于假设时，才需要第二阶段。
只要指定了每个簇的成本和每个个体的成本，就可以轻松计算聚类随机试验的有效样本量。
