A Matlab Framework for Estimation of NLME Models using Stochastic Differential Equations: Applications for estimation of insulin secretion rates



AbstractThe non-linear mixed-effects model based on stochastic differential equations (SDEs) provides an attractive residual error model, that is able to handle serially correlated residuals typically arising from structural mis-specification of the true underlying model. The use of SDEs also opens up for new tools for model development and easily allows for tracking of unknown inputs and parameters over time. An algorithm for maximum likelihood estimation of the model has earlier been proposed, and the present paper presents the first general implementation of this algorithm. The implementation is done in Matlab and also demonstrates the use of parallel computing for improved estimation times. The use of the implementation is illustrated by two examples of application which focus on the ability of the model to estimate unknown inputs
facilitated by the extension to SDEs. The first application is a deconvolution-type estimation of the insulin secretion rate based on a linear two-compartment model for C-peptide measurements. In the second application the model is extended to also give an estimate of the time varying liver extraction based on both C-peptide and insulin measurements.
Keywordsnon-linear mixed-effects modelling; SDE; Kalman smoothing; deconvolution; state estimation; parameter tracking; MatlabMPI; PK non-linear mixed-effects modelling; SDE; Kalman smoothing; deconvolution; state estimation; parameter tracking; MatlabMPI; PK/PD
TypeJournal paper [With referee]
JournalJournal of Pharmacokinetics and Pharmacodynamics
Year2007    Month October    Vol. 34    No. 5    pp. 623-42
PublisherSpringer Netherlands
BibTeX data [bibtex]
IMM Group(s)Mathematical Statistics