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

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

Published in

International Journal of Biostatistics
2017, Volume: 13, number: 2, article number: 20160093
Publisher: WALTER DE GRUYTER GMBH

SLU Authors

• Forkman, Johannes

  • Department of Crop Production Ecology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics


Publication identifier

DOI: https://doi.org/10.1515/ijb-2016-0093

Permanent link to this page (URI)

https://res.slu.se/id/publ/93348