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Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0.
Bazzoli C., Retout S., Mentré F.
Computer Methods and Programs in Biomedicine 98, 1 (2010) 55-65 - http://www.hal.inserm.fr/inserm-00431457/fr/
(19892427)
Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0.
Caroline Bazzoli () 1, Sylvie Retout1, France Mentré1, INSERM, U738, Paris, France Collaboration(s)
1 :  Modèles et méthodes de l'évaluation thérapeutique des maladies chroniques
INSERM : U738 – Université Paris VII - Paris Diderot
Faculté de médecine Paris 7 16, Rue Henri Huchard 75018 Paris
France
Nonlinear mixed effect models (NLMEM) with multiple responses are increasingly used in pharmacometrics, one of the main examples being the joint analysis of the pharmacokinetics (PK) and pharmacodynamics (PD) of a drug. Efficient tools for design evaluation and optimisation in NLMEM are necessary. The R functions PFIM 1.2 and PFIMOPT 1.0 were proposed for these purposes, but accommodate only single response models. The methodology used is based on the Fisher information matrix, developed using a linearisation of the model. In this paper, we present an extended version, PFIM 3.0, dedicated to both design evaluation and optimisation for multiple response models, using a similar method as for single response models. In addition to handling multiple response models, several features have been integrated into PFIM 3.0 for model specification and optimisation. The extension includes a library of classical analytical pharmacokinetics models and allows the user to describe more complex models using differential equations. Regarding the optimisation algorithm, an alternative to the Simplex algorithm has been implemented, the Fedorov-Wynn algorithm to optimise more practical D-optimal design. Indeed, this algorithm optimises design among a set of sampling times specified by the user. This R function is freely available at http://www.pfim.biostat.fr. The efficiency of this approach and the simplicity of use of PFIM 3.0 are illustrated with a real example of the joint PKPD analysis of warfarin, an oral anticoagulant, with a model defined by ordinary differential equations.
Sciences du Vivant/Bio-Informatique, Biostatistique
Informatique/Bio-informatique
Anglais
1872-7565

Articles dans des revues avec comité de lecture
10.1016/j.cmpb.2009.09.012
Computer Methods and Programs in Biomedicine (Comput Methods Programs Biomed)
Publisher Elsevier
ISSN 0169-2607 
internationale
04/2010
04/11/2009
98
1
55-65

Fisher information matrix Nonlinear mixed effect models Multiple response models Optimal designs D-optimality PFIM
Algorithms – Anticoagulants – Computer Simulation – Humans – Models – Statistical – Nonlinear Dynamics – Research Design – Software – Warfarin
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CMPB-D-09-00097-2_HAL.pdf.pdf(1.3 MB)

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