Optimum designs for non-linear mixed effects models in the presence of covariates

Barbara Bogacka; Mahbub A. H. M. Latif; Steven G. Gilmour; Kuresh Youdim

doi:10.1111/biom.12660

Optimum designs for non-linear mixed effects models in the presence of covariates

Barbara Bogacka, Mahbub A. H. M. Latif, Steven G. Gilmour, Kuresh Youdim

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Abstract

In this paper 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 effciently design pre-clinical 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.

Original language	English
Pages (from-to)	927-937
Journal	Biometrics
Volume	73
Issue number	3
Early online date	28 Jan 2017
DOIs	https://doi.org/10.1111/biom.12660
Publication status	Published - Sept 2017

Keywords

Covariates
Enzyme kinetics
Planning experiments
Random model parameters

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10.1111/biom.12660

Optimum Designs for Non-Linear_BOGACKA_Publishedonline28January2017_GREEN AAMAccepted author manuscript, 685 KBLicence: CC BY-NC

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title = "Optimum designs for non-linear mixed effects models in the presence of covariates",

abstract = "In this paper 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 effciently design pre-clinical 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.",

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AU - Bogacka, Barbara

AU - Latif, Mahbub A. H. M.

AU - Gilmour, Steven G.

AU - Youdim, Kuresh

PY - 2017/9

Y1 - 2017/9

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AB - In this paper 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 effciently design pre-clinical 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.

KW - Covariates

KW - Enzyme kinetics

KW - Planning experiments

KW - Random model parameters

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JO - Biometrics

JF - Biometrics

IS - 3

ER -

Optimum designs for non-linear mixed effects models in the presence of covariates

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Keywords

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