Abstract

Intraclass correlation coefficient for data in a clustered study is traditionally estimated from a one-way random-effects model. This model assumes normality for the random cluster effect and the residual effect. When the normality assumption is questionable, we find that the estimated correlation could be much below the nominal level when data are highly skewed or data have low kurtosis. We develop a bias-corrected estimator based on the approach by Thomas and Hultquist for a study with unbalanced cluster sizes. For multivariate normal data or non-normal data with moderate skewness, we compare the performance of the new bias-corrected estimator with two existing estimators with regards to accuracy and precision. When correlation is small, the existing ANOVA estimator works well. When correlation is medium to large, the proposed new estimator has the correlation close to the nominal level, and its mean squared error is smaller than others.

摘要

传统上，聚类研究中数据的组内相关系数是根据单向随机效应模型估计的。该模型假设随机集群效应和残差效应是正态的。当正态性假设有问题时，我们发现当数据高度偏斜或数据具有低峰度时，估计的相关性可能远低于名义水平。我们基于 Thomas 和 Hultquist 的方法开发了一个偏差校正估计器，用于不平衡集群大小的研究。对于具有中等偏度的多元正态数据或非正态数据，我们将新的偏差校正估计器的性能与两个现有估计器的准确性和精度进行比较。当相关性较小时，现有的 ANOVA 估计器效果很好。当相关性为中到大时，