Du Ruofei, Choi Ye Jin, Lee Ji-Hyun, Songthip Ounpraseuth, Hu Zhuopei
Department of Biostatistics, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Department of Statistics, Ohio State University, Columbus, OH, USA.
Commun Stat Simul Comput. 2024;53(2):1048-1067. doi: 10.1080/03610918.2022.2039396. Epub 2022 Feb 23.
Small number of clusters combined with cluster level heterogeneity poses a great challenge for the data analysis. We have published a weighted Jackknife approach to address this issue applying weighted cluster means as the basic estimators. The current study proposes a new version of the weighted delete-one-cluster Jackknife analytic framework, which employs Ordinary Least Squares or Generalized Least Squares estimators as the fundamentals. Algorithms for computing estimated variances of the study estimators have also been derived. Wald test statistics can be further obtained, and the statistical comparison in the outcome means of two conditions is determined using the cluster permutation procedure. The simulation studies show that the proposed framework produces estimates with higher precision and improved power for statistical hypothesis testing compared to other methods.
少量聚类与聚类水平的异质性给数据分析带来了巨大挑战。我们已经发表了一种加权刀切法来解决这个问题，该方法将加权聚类均值作为基本估计量。当前的研究提出了一种新版本的加权删除一个聚类的刀切分析框架，该框架采用普通最小二乘法或广义最小二乘法估计量作为基础。还推导了计算研究估计量估计方差的算法。可以进一步获得 Wald 检验统计量，并使用聚类排列程序确定两种条件下结果均值的统计比较。模拟研究表明，与其他方法相比，所提出的框架产生的估计具有更高的精度和改进的统计假设检验功效。
