Maximum likelihood estimation of long-term HIV dynamic models and antiviral response

Marc Lavielle 1, * Adeline Samson 2 Ana Karina Fermin 1 France Mentré 3
* Auteur correspondant
1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : HIV dynamics studies, based on differential equations, have significantly improved the knowledge on HIV infection. While first studies used simplified short-term dynamic models, recent works considered more complex long-term models combined with a global analysis of whole patient data based on nonlinear mixed models, increasing the accuracy of the HIV dynamic analysis. However statistical issues remain, given the complexity of the problem. We proposed to use the SAEM (stochastic approximation expectation-maximization) algorithm, a powerful maximum likelihood estimation algorithm, to analyze simultaneously the HIV viral load decrease and the CD4 increase in patients using a long-term HIV dynamic system. We applied the proposed methodology to the prospective COPHAR2-ANRS 111 trial. Very satisfactory results were obtained with a model with latent CD4 cells defined with five differential equations. One parameter was fixed, the 10 remaining parameters (eight with between-patient variability) of this model were well estimated. We showed that the efficacy of nelfinavir was reduced compared to indinavir and lopinavir.
Type de document :
Article dans une revue
Biometrics, Wiley, 2011, 67 (1), pp.250-9. 〈10.1111/j.1541-0420.2010.01422.x〉
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Soumis le : jeudi 27 mai 2010 - 12:38:25
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Marc Lavielle, Adeline Samson, Ana Karina Fermin, France Mentré. Maximum likelihood estimation of long-term HIV dynamic models and antiviral response. Biometrics, Wiley, 2011, 67 (1), pp.250-9. 〈10.1111/j.1541-0420.2010.01422.x〉. 〈inserm-00486937〉



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