Monolix : analysis of non linear mixed effects models

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
Higher Edu - Research dev card
  • Creation or important update: 28/03/11
  • Minor correction: 21/05/19
  • Index card author: fenix-contact fenix-contact (CNRS)
  • Theme leader: Damien Ferney (Laboratoire de mathématiques - Clermont-Ferrand)
Keywords
General software features

MONOLIX is a free software dedicated to the analysis of non linear mixed effects models. The objective of this software is to perform: parameter estimation, model selection, goodness of fit plots and, data simulation.

Context in which the software is used
  • Research in statistic: University of Paris 5, 11 and 13
  • Research in pharmacology: INSERM - P7
  • Research in microbiology : INRA
Publications related to software

SAEM algorithm

  • Delyon B., Lavielle M., and Moulines E. "Convergence of a stochastic approximation version of the EM algorithm" The Annals of Stat., vol 27, no. 1, pp 94-128, 1999.
  • Kuhn E., Lavielle M. "Coupling a stochastic approximation version of EM with a MCMC procedure" ESAIM P&S, vol.8, pp 115-131, 2004.
  • Kuhn E., Lavielle M. "Maximum likelihood estimation in nonlinear mixed effects models" Computational Statistics and Data Analysis, vol. 49, No. 4, pp 1020-1038, 2005.
  • Lavielle M., Meza C. "A Parameter Expansion version of the SAEM algorithm" Statistics and Computing, vol. 17, pp 121-130, 2007.
  • Donnet S., Samson A. "Estimation of parameters in incomplete data models defined by dynamical systems" Jour. of Stat. Planning and Inference, vol. 137, no. 9, pp 2815-2831, 2007.
  • Meza C., Jaffrezic F., Foulley J.L. "REML estimation of variance parameters in non linear mixed effects models using the SAEM algorithm" The Biometrical Journal 49, 1-13, 2007.
  • Donnet S., Samson A. "Parametric inference for mixed models defined by stochastic differential equations" ESAIM P&S, 12:196-218, (2008).

Applications

  • Makowski D., Lavielle M. "Using SAEM to estimate parameters of models of response to applied fertilizer" Journal of agricultural, Biological and Enviromental Statistics, vol. 11, n. 1, pp. 45-60, 2006.
  • Samson A., Lavielle M., Mentré F. "Extension of the SAEM algorithm to left-censored data in non-linear mixed-effects model: application to HIV dynamics models" Computational Statistics and Data Analysis, vol. 51, pp. 1562--1574, 2006.
  • Jaffrezic F., Meza C., Lavielle M., Foulley J.L. "Genetics analysis of growth curves using the SAEM algorithm" Genetics Selection Evolution, vol. 38, pp. 583--600, 2006.
  • Lavielle M., Mentré F. "Estimation of population pharmacokinetic parameters of saquinavir in HIV patients and covariate analysis with the SAEM algorithm" Journal of Pharmacokinetics and Pharmacodynamics, vol. 34, pp. 229--49, 2007.
  • Comets E, Verstuyft C, Lavielle M, Jaillon P, Becquemont L, Mentré F. Modelling the influence of MDR1 polymorphism on digoxin pharmacokinetic parameters. European Journal of Clinical Pharmacology, 63, pp. 437-49, 2007.
  • Samson A., Lavielle M., Mentré F. "The SAEM algorithm for group comparison tests in longitudinal data analysis based on nonlinear mixed-effects model" Statistics in Medicine, vol. 26, pp 4860-4875, 2007.