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
1-17
Latif, A.M.
01d688d6-5de4-4658-8f46-dac486b82a65
Gilmour, S.G.
984dbefa-893b-444d-9aa2-5953cd1c8b03
17 July 2014
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, .
(doi:10.1177/0962280214544017).
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.
Text
tbsSMMRv2.pdf
- Accepted Manuscript
Available under License Other.
More information
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: http://eprints.soton.ac.uk/id/eprint/367196
ISSN: 0962-2802
PURE UUID: 327d34d8-a64b-4d18-bad7-8824fd8b7f32
Catalogue record
Date deposited: 23 Jul 2014 14:51
Last modified: 14 Mar 2024 17:25
Export record
Altmetrics
Contributors
Author:
A.M. Latif
Author:
S.G. Gilmour
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics