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Optimum designs for non‐linear mixed effects models in the presence of covariates
Author(s) -
Bogacka Barbara,
Latif Mahbub A. H. M.,
Gilmour Steven G.,
Youdim Kuresh
Publication year - 2017
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12660
Subject(s) - covariate , mixed model , generalized linear mixed model , statistics , linear model , econometrics , computer science , mathematics
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.