Eldridge Sandra M, Ashby Deborah, Kerry Sally
Centre for Health Sciences, Barts and The London School of Medicine and Dentistry, Queen Mary, University of London, London, UK.
Int J Epidemiol. 2006 Oct;35(5):1292-300. doi: 10.1093/ije/dyl129. Epub 2006 Aug 30.
Cluster randomized trials are increasingly popular. In many of these trials, cluster sizes are unequal. This can affect trial power, but standard sample size formulae for these trials ignore this. Previous studies addressing this issue have mostly focused on continuous outcomes or methods that are sometimes difficult to use in practice.
We show how a simple formula can be used to judge the possible effect of unequal cluster sizes for various types of analyses and both continuous and binary outcomes. We explore the practical estimation of the coefficient of variation of cluster size required in this formula and demonstrate the formula's performance for a hypothetical but typical trial randomizing UK general practices.
The simple formula provides a good estimate of sample size requirements for trials analysed using cluster-level analyses weighting by cluster size and a conservative estimate for other types of analyses. For trials randomizing UK general practices the coefficient of variation of cluster size depends on variation in practice list size, variation in incidence or prevalence of the medical condition under examination, and practice and patient recruitment strategies, and for many trials is expected to be approximately 0.65. Individual-level analyses can be noticeably more efficient than some cluster-level analyses in this context.
When the coefficient of variation is <0.23, the effect of adjustment for variable cluster size on sample size is negligible. Most trials randomizing UK general practices and many other cluster randomized trials should account for variable cluster size in their sample size calculations.
整群随机试验越来越普遍。在许多此类试验中，群大小不相等。这可能会影响试验效能，但这些试验的标准样本量公式忽略了这一点。以往针对该问题的研究大多集中在连续型结局或实际应用中有时难以使用的方法上。
我们展示了如何使用一个简单公式来判断不同类型分析以及连续型和二分类结局中群大小不相等可能产生的影响。我们探讨了该公式所需的群大小变异系数的实际估计方法，并展示了该公式在一项将英国普通诊所随机分组的假设但典型试验中的性能。
该简单公式为使用按群大小加权的群水平分析进行分析的试验提供了良好的样本量需求估计，而对其他类型分析则提供了保守估计。对于将英国普通诊所随机分组的试验，群大小变异系数取决于诊所名单大小的变异、所研究疾病的发病率或患病率的变异以及诊所和患者招募策略，并且对于许多试验预计约为0.65。在此背景下，个体水平分析可能比某些群水平分析明显更有效。
当变异系数<0.23时，对可变群大小进行调整对样本量的影响可忽略不计。大多数将英国普通诊所随机分组的试验以及许多其他整群随机试验在计算样本量时应考虑可变群大小。
