Sample size calculation for stepped wedge and other longitudinal cluster randomised trials
Sample size calculation for stepped wedge and other longitudinal cluster randomised trials
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DOI：
10.1002/sim.7028
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发表时间：
2016-10-01
影响因子：
2
通讯作者：
Eldridge, Sandra
中科院分区：
文献类型：
--
作者：
Hooper, Richard;Teerenstra, Steven;Eldridge, Sandra
与个体随机试验相比，群集随机试验所需的样本量被夸大，因为来自同一群集的参与者的结果是相关的。纵向整群随机试验（包括阶梯楔形试验）的样本量计算需要考虑至少两个水平的聚类：聚类本身和聚类内的时间。我们推导出公式的样本量为重复的横截面和封闭队列群集随机试验的正态分布的结果的措施，在多层次模型允许集群之间的变化和集群内的时间。我们的公式同意与先前描述的特殊情况下，如交叉和协方差设计分析，虽然模拟表明，公式可能会低估所需的样本量时，集群的数量是小的。无论是使用公式还是模拟，样本量计算都需要估计滋扰参数，在我们的模型中，这些参数包括群内相关性、群自相关性和个体自相关性。小于1的聚类自相关反映了在不同时间从同一聚类中采样的个体比在同一时间从同一聚类中采样的个体具有更少的相关结果的情况。营养学参数可以从具有相同结果测量的相似聚类设置中获得的时间序列中估计，使用方差分析来估计方差分量。版权所有（C）2016约翰威利父子有限公司
The sample size required for a cluster randomised trial is inflated compared with an individually randomised trial because outcomes of participants from the same cluster are correlated. Sample size calculations for longitudinal cluster randomised trials ( including stepped wedge trials) need to take account of at least two levels of clustering: the clusters themselves and times within clusters. We derive formulae for sample size for repeated cross-section and closed cohort cluster randomised trials with normally distributed outcome measures, under a multilevel model allowing for variation between clusters and between times within clusters. Our formulae agree with those previously described for special cases such as crossover and analysis of covariance designs, although simulation suggests that the formulae could underestimate required sample size when the number of clusters is small. Whether using a formula or simulation, a sample size calculation requires estimates of nuisance parameters, which in our model include the intracluster correlation, cluster autocorrelation, and individual autocorrelation. A cluster autocorrelation less than 1 reflects a situation where individuals sampled from the same cluster at different times have less correlated outcomes than individuals sampled from the same cluster at the same time. Nuisance parameters could be estimated from time series obtained in similarly clustered settings with the same outcome measure, using analysis of variance to estimate variance components. Copyright (C) 2016 John Wiley & Sons, Ltd.