Princess Margaret Hospital, Toronto, Canada.
BMC Med Res Methodol. 2012 Aug 20;12:126. doi: 10.1186/1471-2288-12-126.
Intraclass correlation coefficients (ICCs) are used in a wide range of applications. However, most commonly used estimators for the ICC are known to be subject to bias.
Using second order Taylor series expansion, we propose a new bias-corrected estimator for one type of intraclass correlation coefficient, for the ICC that arises in the context of the balanced one-way random effects model. A simulation study is performed to assess the performance of the proposed estimator. Data have been generated under normal as well as non-normal scenarios.
Our simulation results show that the new estimator has reduced bias compared to the least square estimator which is often referred to as the conventional or analytical estimator. The results also show marked bias reduction both in normal and non-normal data scenarios. In particular, our estimator outperforms the analytical estimator in a non-normal setting producing estimates that are very close to the true ICC values.
The proposed bias-corrected estimator for the ICC from a one-way random effects analysis of variance model appears to perform well in the scenarios we considered in this paper and can be used as a motivation to construct bias-corrected estimators for other types of ICCs that arise in more complex scenarios. It would also be interesting to investigate the bias-variance trade-off.
组内相关系数（ICCs）在广泛的应用中被使用。然而，最常用的 ICC 估计量存在偏差。
我们使用二阶泰勒级数展开，提出了一种新的用于平衡单向随机效应模型中出现的 ICC 的有偏校正估计量。进行了模拟研究以评估所提出的估计量的性能。在正态和非正态情况下生成了数据。
我们的模拟结果表明，与通常称为常规或分析估计量的最小二乘估计量相比，新估计量的偏差较小。结果还表明，在正态和非正态数据情况下，偏差明显减小。特别是，我们的估计量在非正态情况下表现优于分析估计量，产生的估计值非常接近真实的 ICC 值。
从方差分析模型的单向随机效应分析中提出的 ICC 的有偏校正估计量在本文考虑的情况下似乎表现良好，可以作为构建更复杂情况下出现的其他类型 ICC 的有偏校正估计量的动力。研究偏差-方差权衡也将很有趣。
