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

Forkman Johannes ()
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Forkman Johannes: Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Box 7043, SE-750 07Uppsala, Sweden

The International Journal of Biostatistics, 2017, vol. 13, issue 2, 14

Abstract: 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.

Keywords: bioanalytical method validation; generalized pivotal quantity; linear mixed model; semiparametric mixed-effects model; split-plot experiment (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/ijb-2016-0093

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