title: Optimum designs for non-linear mixed effects models in the presence of covariates.
abstract: In this article, we present a new method for optimizing designs of experiments for non-linear mixed effects models, where a categorical factor with covariate information is a design variable combined with another design factor. The work is motivated by the need to efficiently design preclinical experiments in enzyme kinetics for a set of Human Liver Microsomes. However, the results are general and can be applied to other experimental situations where the variation in the response due to a categorical factor can be partially accounted for by a covariate. The covariate included in the model explains some systematic variability in a random model parameter. This approach allows better understanding of the population variation as well as estimation of the model parameters with higher precision.
authors.affiliation: School of Mathematical Sciences, Queen Mary, University of London, London E1 4NS, UK.
authors.forename: Barbara
authors.initials: B
authors.lastname: Bogacka
authors.affiliation: Institute of Statistical Research and Training, University of Dhaka, Dhaka-1000, Bangladesh.|Center for Clinical Epidemiology, St Luke's International University, 3-6-2 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan.
authors.forename: Mahbub A H M
authors.initials: MAHM
authors.lastname: Latif
authors.affiliation: Department of Mathematics, King's College London, Strand, London WC2R 2LS, UK.
authors.forename: Steven G
authors.initials: SG
authors.lastname: Gilmour
authors.affiliation: Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland.
authors.forename: Kuresh
authors.initials: K
authors.lastname: Youdim
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008862:Microsomes, Liver; D017711:Nonlinear Dynamics
keywords: Design of experiments; Enzyme kinetics; Fisher Information; Planning experiments; Random effects model