Université de Tours, Université de Nantes, INSERM, Tours, France.
INSERM CIC1415, CHRU de Tours, Tours, France.
Stat Methods Med Res. 2021 Aug;30(8):1988-2003. doi: 10.1177/09622802211026004. Epub 2021 Jul 4.
In cluster randomised trials, a measure of intracluster correlation such as the intraclass correlation coefficient (ICC) should be reported for each primary outcome. Providing intracluster correlation estimates may help in calculating sample size of future cluster randomised trials and also in interpreting the results of the trial from which they are derived. For a binary outcome, the ICC is known to be associated with its prevalence, which raises at least two issues. First, it questions the use of ICC estimates obtained on a binary outcome in a trial for sample size calculations in a subsequent trial in which the same binary outcome is expected to have a different prevalence. Second, it challenges the interpretation of ICC estimates because they do not solely depend on clustering level. Other intracluster correlation measures proposed for clustered binary data settings include the variance partition coefficient, the median odds ratio and the tetrachoric correlation coefficient. Under certain assumptions, the theoretical maximum possible value for an ICC associated with a binary outcome can be derived, and we proposed the relative deviation of an ICC estimate to this maximum value as another measure of the intracluster correlation. We conducted a simulation study to explore the dependence of these intracluster correlation measures on outcome prevalence and found that all are associated with prevalence. Even if all depend on prevalence, the tetrachoric correlation coefficient computed with Kirk's approach was less dependent on the outcome prevalence than the other measures when the intracluster correlation was about 0.05. We also observed that for lower values, such as 0.01, the analysis of variance estimator of the ICC is preferred.
在整群随机试验中，应报告每个主要结局的簇内相关测量值，如组内相关系数（ICC）。提供簇内相关估计值可能有助于计算未来整群随机试验的样本量，也有助于解释从中得出的试验结果。对于二分类结局，ICC 与结局的发生率有关，这至少带来两个问题。首先，它质疑了在具有不同发生率的后续试验中，使用试验中获得的二分类结局的 ICC 估计值进行样本量计算的合理性。其次，它对 ICC 估计值的解释提出了挑战，因为它们不仅取决于聚类水平。针对聚类二分类数据情况，还提出了其他簇内相关测量值，包括方差分解系数、中位数优势比和四分相关系数。在某些假设下，可以推导出与二分类结局相关的 ICC 的理论最大可能值，我们提出将 ICC 估计值与该最大值的相对偏差作为另一种簇内相关测量值。我们进行了一项模拟研究，以探讨这些簇内相关测量值与结局发生率的依赖性，并发现所有测量值都与发生率有关。即使所有测量值都依赖于发生率，当簇内相关系数约为 0.05 时，Kirk 方法计算的四分相关系数比其他测量值对结局发生率的依赖性更小。我们还观察到，对于较低的值，如 0.01，ICC 的方差分析估计值更可取。
