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Design in nonlinear mixed effects models: Optimization using the Fedorov-Wynn algorithm and power of the Wald test for binary covariates.
Retout S., Comets E., Samson A., Mentré F.
Statistics in Medicine (2007) 2007 - http://www.hal.inserm.fr/inserm-00150430/fr/
(17486667)
Design in nonlinear mixed effects models: Optimization using the Fedorov-Wynn algorithm and power of the Wald test for binary covariates.
Sylvie Retout () 1, 2, Emmanuelle Comets1, Adeline Samson1, France Mentré1, 2
1 :  Modèles et méthodes de l'évaluation thérapeutique des maladies chroniques
INSERM : U738 – Université Paris-Diderot - Paris VII
Faculté de médecine Paris 7 16, Rue Henri Huchard 75018 Paris
France
2 :  Département d'épidémiologie, biostatistique et recherche clinique
AP-HP – Hôpital Bichat - Claude Bernard
46 rue Henri Huchard 75018 Paris
France
We extend the methodology for designs evaluation and optimization in nonlinear mixed effects models with an illustration of the decrease of human immunodeficiency virus viral load after antiretroviral treatment initiation described by a bi-exponential model. We first show the relevance of the predicted standard errors (SEs) given by the computation of the population Fisher information matrix using the R function PFIM, in comparison to those computed with the stochastic approximation expectation-maximization algorithm, implemented in the Monolix software. We then highlight the usefulness of the Fedorov-Wynn (FW) algorithm for designs optimization compared to the Simplex algorithm. From the predicted SE of PFIM, we compute the predicted power of the Wald test to detect a treatment effect as well as the number of subjects needed to achieve a given power. Using the FW algorithm, we investigate the influence of the design on the power and show that, for optimized designs with the same total number of samples, the power increases when the number of subjects increases and the number of samples per subject decreases. A simulation study is also performed with the nlme function of R to confirm this result and show the relevance of the predicted powers compared to those observed by simulation. Copyright (c) 2007 John Wiley & Sons, Ltd.
Sciences du Vivant/Bio-Informatique, Biostatistique
Anglais
0277-6715

Articles dans des revues avec comité de lecture
10.1002/sim.2910
Statistics in Medicine (Stat Med)
Publisher John Wiley & Sons
ISSN 0277-6715 (eISSN : 1097-0258)
08/05/2007
08/05/2007
2007


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