Guittet Lydia, Ravaud Philippe, Giraudeau Bruno
Département d'Epidémiologie, Biostatistique et Recherche Clinique, Groupe Hospitalier Bichat-Claude Bernard (AP-HP), Université Xavier Bichat, Paris, France.
BMC Med Res Methodol. 2006 Apr 12;6:17. doi: 10.1186/1471-2288-6-17.
Cluster randomization design is increasingly used for the evaluation of health-care, screening or educational interventions. At the planning stage, sample size calculations usually consider an average cluster size without taking into account any potential imbalance in cluster size. However, there may exist high discrepancies in cluster sizes.
We performed simulations to study the impact of an imbalance in cluster size on power. We determined by simulations to which extent four methods proposed to adapt the sample size calculations to a pre-specified imbalance in cluster size could lead to adequately powered trials.
We showed that an imbalance in cluster size can be of high influence on the power in the case of severe imbalance, particularly if the number of clusters is low and/or the intraclass correlation coefficient is high. In the case of a severe imbalance, our simulations confirmed that the minimum variance weights correction of the variation inflaction factor (VIF) used in the sample size calculations has the best properties.
Publication of cluster sizes is important to assess the real power of the trial which was conducted and to help designing future trials. We derived an adaptation of the VIF from the minimum variance weights correction to be used in case the imbalance can be a priori formulated such as "a proportion (gamma) of clusters actually recruit a proportion (tau) of subjects to be included (gamma < or = tau)".
整群随机化设计越来越多地用于评估医疗保健、筛查或教育干预措施。在规划阶段，样本量计算通常考虑平均整群大小，而不考虑整群大小的任何潜在不平衡。然而，整群大小可能存在很大差异。
我们进行了模拟研究整群大小不平衡对检验效能的影响。我们通过模拟确定了为使样本量计算适应预先指定的整群大小不平衡而提出的四种方法在多大程度上可导致检验效能充足的试验。
我们表明，在严重不平衡的情况下，整群大小不平衡可能对检验效能有很大影响，特别是当整群数量较少和/或组内相关系数较高时。在严重不平衡的情况下，我们的模拟证实，样本量计算中使用的变异膨胀因子（VIF）的最小方差权重校正具有最佳特性。
公布整群大小对于评估所进行试验的实际检验效能以及帮助设计未来试验很重要。我们从最小方差权重校正中推导了VIF的一种调整方法，以便在不平衡可以先验确定的情况下使用，例如“一定比例（γ）的整群实际招募了一定比例（τ）的纳入受试者（γ≤τ）”。
