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Extension of the SAEM algorithm for nonlinear mixed models with 2 levels of random effects.
Panhard X., Samson A.
Biostatistics 10, 1 (2009) 121-35 - http://www.hal.inserm.fr/inserm-00383201/fr/
 (18583352) 
Extension of the SAEM algorithm for nonlinear mixed models with 2 levels of random effects.
Xavière Panhard () 1, Adeline Samson2
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
2 :  MAP5 - Mathématiques appliquées Paris 5
http://www.math-info.univ-paris5.fr/map5/
CNRS : UMR8145 – Université Paris V - Paris Descartes
UFR de Maths et informatique 45 rue des Saints Pères 75270 PARIS CEDEX 06
France
This article focuses on parameter estimation of multilevel nonlinear mixed-effects models (MNLMEMs). These models are used to analyze data presenting multiple hierarchical levels of grouping (cluster data, clinical trials with several observation periods, ...). The variability of the individual parameters of the regression function is thus decomposed as a between-subject variability and higher levels of variability (e.g. within-subject variability). We propose maximum likelihood estimates of parameters of those MNLMEMs with 2 levels of random effects, using an extension of the stochastic approximation version of expectation-maximization (SAEM)-Monte Carlo Markov chain algorithm. The extended SAEM algorithm is split into an explicit direct expectation-maximization (EM) algorithm and a stochastic EM part. Compared to the original algorithm, additional sufficient statistics have to be approximated by relying on the conditional distribution of the second level of random effects. This estimation method is evaluated on pharmacokinetic crossover simulated trials, mimicking theophylline concentration data. Results obtained on those data sets with either the SAEM algorithm or the first-order conditional estimates (FOCE) algorithm (implemented in the nlme function of R software) are compared: biases and root mean square errors of almost all the SAEM estimates are smaller than the FOCE ones. Finally, we apply the extended SAEM algorithm to analyze the pharmacokinetic interaction of tenofovir on atazanavir, a novel protease inhibitor, from the Agence Nationale de Recherche sur le Sida 107-Puzzle 2 study. A significant decrease of the area under the curve of atazanavir is found in patients receiving both treatments.
Sciences du Vivant/Bio-Informatique, Biostatistique
Informatique/Bio-informatique
Anglais
1465-4644

Articles dans des revues avec comité de lecture
10.1093/biostatistics/kxn020
Biostatistics (Biostatistics)
Publisher Oxford University Press (OUP): Policy B
ISSN 1465-4644 (eISSN : 1468-4357)
internationale
01/2009
25/06/2008
10
1
121-35

Multilevel nonlinear mixed effects models – SAEM algorithm – Multiple periods – Cross-over trial – Bioequivalence trials.
Algorithms – Anti-HIV Agents – Area Under Curve – Bias (Epidemiology) – Biometry – Cluster Analysis – Cross-Over Studies – Drug Interactions – Humans – Likelihood Functions – Markov Chains – Monte Carlo Method – Nonlinear Dynamics – Oligopeptides – Pyridines – Regression Analysis – Theophylline – Therapeutic Equivalency – Time Factors
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Reviewed_L07-164_PanhardSamson.pdf(523.1 KB)
inserm-00383201_edited.pdf(666.9 KB)
ANNEX
supplementary_L07-164R.pdf(47.7 KB)
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index.xhtml(91.9 KB)

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