Forkman Johannes
.
Int J Biostat. 2017 Jun 15;13(2):/j/ijb.2017.13.issue-2/ijb-2016-0093/ijb-2016-0093.xml. doi: 10.1515/ijb-2016-0093.
Linear mixed-effects models are linear models with several variance components. Models with a single random-effects factor have two variance components: the random-effects variance, i. e., the inter-subject variance, and the residual error variance, i. e., the intra-subject variance. In many applications, it is practice to report variance components as coefficients of variation. The intra- and inter-subject coefficients of variation are the square roots of the corresponding variances divided by the mean. This article proposes methods for computing confidence intervals for intra- and inter-subject coefficients of variation using generalized pivotal quantities. The methods are illustrated through two examples. In the first example, precision is assessed within and between runs in a bioanalytical method validation. In the second example, variation is estimated within and between main plots in an agricultural split-plot experiment. Coverage of generalized confidence intervals is investigated through simulation and shown to be close to the nominal value.
线性混合效应模型是具有多个方差分量的线性模型。具有单个随机效应因子的模型有两个方差分量：随机效应方差，即个体间方差，以及残差误差方差，即个体内方差。在许多应用中，通常将方差分量报告为变异系数。个体内和个体间变异系数分别是相应方差除以均值后的平方根。本文提出了使用广义枢轴量计算个体内和个体间变异系数置信区间的方法。通过两个例子对这些方法进行了说明。在第一个例子中，评估了生物分析方法验证中批次内和批次间的精密度。在第二个例子中，估计了农业裂区试验中主区组内和主区组间的变异。通过模拟研究了广义置信区间的覆盖率，结果表明其接近名义值。
