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Transform-both-sides nonlinear models for in vitro pharmacokinetic experiments

Transform-both-sides nonlinear models for in vitro pharmacokinetic experiments
Transform-both-sides nonlinear models for in vitro pharmacokinetic experiments
Transform-both-sides nonlinear models have proved useful in many experimental applications including those in pharmaceutical sciences and biochemistry. The maximum likelihood method is commonly used to fit transform-both-sides nonlinear models, where the regression and transformation parameters are estimated simultaneously. In this paper, an analysis of variance-based method is described in detail for estimating transform-both-sides nonlinear models from randomized experiments. It estimates the transformation parameter from the full treatment model and then the regression parameters are estimated conditionally on this estimate of the transformation parameter. The analysis of variance method is computationally simpler compared with the maximum likelihood method of estimation and allows a more natural separation of different sources of lack of fit. Simulation studies show that the analysis of variance method can provide unbiased estimators of complex transform-both-sides nonlinear models, such as transform-both-sides random coefficient nonlinear regression models and transform-both-sides fixed coefficient nonlinear regression models with random block effects.
nonlinear mixed effects model, pure error and lack of fit, random block effects
0962-2802
1-17
Latif, A.M.
01d688d6-5de4-4658-8f46-dac486b82a65
Gilmour, S.G.
984dbefa-893b-444d-9aa2-5953cd1c8b03
Latif, A.M.
01d688d6-5de4-4658-8f46-dac486b82a65
Gilmour, S.G.
984dbefa-893b-444d-9aa2-5953cd1c8b03

Latif, A.M. and Gilmour, S.G. (2014) Transform-both-sides nonlinear models for in vitro pharmacokinetic experiments. Statistical Methods in Medical Research, 1-17. (doi:10.1177/0962280214544017).

Record type: Article

Abstract

Transform-both-sides nonlinear models have proved useful in many experimental applications including those in pharmaceutical sciences and biochemistry. The maximum likelihood method is commonly used to fit transform-both-sides nonlinear models, where the regression and transformation parameters are estimated simultaneously. In this paper, an analysis of variance-based method is described in detail for estimating transform-both-sides nonlinear models from randomized experiments. It estimates the transformation parameter from the full treatment model and then the regression parameters are estimated conditionally on this estimate of the transformation parameter. The analysis of variance method is computationally simpler compared with the maximum likelihood method of estimation and allows a more natural separation of different sources of lack of fit. Simulation studies show that the analysis of variance method can provide unbiased estimators of complex transform-both-sides nonlinear models, such as transform-both-sides random coefficient nonlinear regression models and transform-both-sides fixed coefficient nonlinear regression models with random block effects.

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e-pub ahead of print date: 17 July 2014
Published date: 17 July 2014
Keywords: nonlinear mixed effects model, pure error and lack of fit, random block effects
Organisations: Mathematical Sciences

Identifiers

Local EPrints ID: 367196
URI: https://eprints.soton.ac.uk/id/eprint/367196
ISSN: 0962-2802
PURE UUID: 327d34d8-a64b-4d18-bad7-8824fd8b7f32

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Date deposited: 23 Jul 2014 14:51
Last modified: 28 Aug 2019 18:48

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