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Intraclass correlation – A discussion and demonstration of basic features

机译:类内关联–基本功能的讨论和演示

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摘要

A re-analysis of intraclass correlation (ICC) theory is presented together with Monte Carlo simulations of ICC probability distributions. A partly revised and simplified theory of the single-score ICC is obtained, together with an alternative and simple recipe for its use in reliability studies. Our main, practical conclusion is that in the analysis of a reliability study it is neither necessary nor convenient to start from an initial choice of a specified statistical model. Rather, one may impartially use all three single-score ICC formulas. A near equality of the three ICC values indicates the absence of bias (systematic error), in which case the classical (one-way random) ICC may be used. A consistency ICC larger than absolute agreement ICC indicates the presence of non-negligible bias; if so, classical ICC is invalid and misleading. An F-test may be used to confirm whether biases are present. From the resulting model (without or with bias) variances and confidence intervals may then be calculated. In presence of bias, both absolute agreement ICC and consistency ICC should be reported, since they give different and complementary information about the reliability of the method. A clinical example with data from the literature is given.
机译:提出了类内相关性(ICC)理论的重新分析以及ICC概率分布的蒙特卡罗模拟。获得了单分数ICC的部分修订和简化的理论,以及在可靠性研究中使用它的另一种简单方法。我们的主要实际结论是,在对可靠性研究进行分析时,从指定统计模型的初始选择开始既没有必要,也没有便利。相反,可以公正地使用所有三个单得分ICC公式。三个ICC值几乎相等,表示不存在偏差(系统误差),在这种情况下,可以使用经典(单向随机)ICC。大于绝对协议ICC的一致性ICC表示存在不可忽略的偏差;如果是这样,则经典ICC无效且具有误导性。 F检验可用于确认是否存在偏差。然后可以从结果模型(无偏差或有偏差)计算方差和置信区间。如果存在偏差,则应报告绝对一致性ICC和一致性ICC,因为它们会提供有关该方法可靠性的不同且互补的信息。给出了具有来自文献的数据的临床实例。

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