Yang Zhao, Sun Xuezheng, Hardin James W
Quintiles, Inc., 5927 S Miami Blvd, Morrisville, NC, 27560, USA.
Pharm Stat. 2012 Sep-Oct;11(5):386-93. doi: 10.1002/pst.1523. Epub 2012 Jun 11.
Although there are several available test statistics to assess the difference of marginal probabilities in clustered matched-pair binary data, associated confidence intervals (CIs) are not readily available. Herein, the construction of corresponding CIs is proposed, and the performance of each CI is investigated. The results from Monte Carlo simulation study indicate that the proposed CIs perform well in maintaining the nominal coverage probability: for small to medium numbers of clusters, the intracluster correlation coefficient-adjusted McNemar statistic and its associated Wald or Score CIs are preferred; however, this statistic becomes conservative when the number of clusters is larger so that alternative statistics and their associated CIs are preferred. In practice, a combination of the intracluster correlation coefficient-adjusted McNemar statistic with an alternative statistic is recommended. To illustrate the practical application, a real clustered matched-pair collection of data is used to illustrate testing the difference of marginal probabilities and constructing the associated CIs.
尽管有几种可用的检验统计量来评估聚类匹配对二元数据中边际概率的差异，但相关的置信区间（CIs）却不易获得。在此，提出了相应置信区间的构建方法，并研究了每个置信区间的性能。蒙特卡罗模拟研究结果表明，所提出的置信区间在维持名义覆盖概率方面表现良好：对于中小数量的聚类，聚类内相关系数调整后的 McNemar 统计量及其相关的 Wald 或 Score 置信区间是首选；然而，当聚类数量较大时，该统计量会变得保守，因此首选替代统计量及其相关的置信区间。在实际应用中，建议将聚类内相关系数调整后的 McNemar 统计量与替代统计量结合使用。为了说明实际应用，使用了一个实际的聚类匹配对数据集来阐述检验边际概率的差异并构建相关的置信区间。
