Analysing population PK/PD data with MONOLIX 2.4
Marc Lavielle (1), Hector Mesa (1), Kaelig Chatel (1), Julie Bertrand (2), France Mentré (2) and the Monolix group
(1) INRIA Saclay, (2) INSERM U738
MONOLIX is an open-source software using Matlab. The full Matlab version and a stand-alone version of MONOLIX can be downloaded from the MONOLIX website : http://software.monolix.org/
MONOLIX performs maximum likelihood estimation in nonlinear mixed effects models without linearization. The algorithms used in MONOLIX combine the SAEM (stochastic approximation version of EM) algorithm with MCMC (Markov Chain Monte Carlo) and a Simulated Annealing procedure. The convergence of this algorithm and its good statistical properties have been proven and published in the best statistical journals [1,2]. The algorithm is fast and efficient in practice. It converges in situations where other reference methods (NONMEM, nlme,...) do not.
A beta version of release 2.4 will be available on the MONOLIX website at the end of June 2008. This last version of MONOLIX 2 will contain many important features:
- An extensive library of PK model (1, 2 and 3 cpts ; effect compartment ; bolus, infusion, oral0 and oral1 absorption ; linear and nonlinear elimination ; single dose, multiple doses and steady state)
- An extensive library of PD models (immediate and turn-over response models ; disease models)
- Continuous and categorical covariate models,
- Constant, proportional, combined and exponential error models,
- Modelisation of the inter-occasion variability,
- Use of several distributions for the individual parameters (normal, lognormal, logit, probit, Box & Cox, ...)
- Model selection: information criteria (AIC, BIC) and statistical tests (LRT, Wald test)
- Data in NONMEM format,
- Goodness of fit plots (VPC, weighted residuals, NPDE, ...),
- Data simulation,
- Automatic reporting,
- A C++ ODEs solver package for user defined models,
- A first version of MLXTRAN (a NMTRAN-like interpreter)
 Delyon B, Lavielle M and Moulines E. "Convergence of a stochastic approximation version of the EM algorithm", Annals of Statistics. 27, 94- 128, 1999.
 Kuhn E and Lavielle M. "Maximum likelihood estimation in nonlinear mixed effects model", Computational Statistics and Data Analysis. 49, 1020-1038, 2005.
 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.
 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.
 Samson A., Lavielle M., Mentré F. "The SAEM algorithm for group comparison test in longitudinal data analysis based in nonlinear mixed-effects model" Stat. in Medecine, vol. 26, pp. 4860--4875, 2007.