Generalized Confidence Intervals for Intra- and Inter-subject Coefficients of Variation in Linear Mixed-effects Models

• Published in: The international journal of biostatistics Vol. 13; no. 2; pp. 445 - 458
• Main Author: Forkman, Johannes
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 27.11.2017; Walter de Gruyter GmbH
• Subjects: bioanalytical method validation; Biostatistics - methods; Confidence intervals; Data Interpretation, Statistical; generalized pivotal quantity; Humans; linear mixed model; Linear Models; Methods; Models, Statistical; Probability Theory and Statistics; Sannolikhetsteori och statistik; semiparametric mixed-effects model; Split-plot design; split-plot experiment; semiparametric mixed-effects model; bioanalytical method validation; generalized pivotal quantity; linear mixed model; split-plot experiment
• ISSN: 1557-4679; 2194-573X; 1557-4679
• 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.