Optimum designs for non‐linear mixed effects models in the presence of covariates. Issue 3 (28th January 2017)

• Record Type: Journal Article
• Authors: Bogacka, Barbara; Latif, Mahbub A. H. M.; Gilmour, Steven G.; Youdim, Kuresh
• Is Part Of: Biometrics. Volume 73:Issue 3(2017)
• Page Start: 927
• Page End: 937
• Publication Date: 2017-01-28
• Subjects: Design of experiments -- Enzyme kinetics -- Fisher Information -- Planning experiments -- Random effects model; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12660
• Languages: English
• ISSNs: 0006-341X

Abstract

Summary: 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.