Y. Aït-sahalia, Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach, Econometrica, vol.70, issue.1, pp.223-262, 2002.
DOI : 10.1111/1468-0262.00274

Y. Aït-sahalia, Closed-form likelihood expansions for multivariate diffusions, The Annals of Statistics, vol.36, issue.2, pp.906-937, 2008.
DOI : 10.1214/009053607000000622

C. Andrieu, A. Doucet, and R. Holenstein, Particle Markov chain Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.50, issue.3, 2010.
DOI : 10.1111/j.1467-9868.2009.00736.x

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.7033

M. Berglund, M. Sunnåker, M. Adiels, M. Jirstrand, and B. Wennberg, Investigations of a compartmental model for leucine kinetics using non-linear mixed effects models with ordinary and stochastic differential equations, Mathematical Medicine and Biology, vol.29, issue.4, 2011.
DOI : 10.1093/imammb/dqr021

O. Cappé, E. Moulines, and T. Ryden, Inference in Hidden Markov Models, 2005.

C. Cuenod, B. Favetto, V. Genon-catalot, Y. Rozenholc, and A. Samson, Parameter estimation and change-point detection from Dynamic Contrast Enhanced MRI data using stochastic differential equations, Mathematical Biosciences, vol.233, issue.1, pp.1-76, 2011.
DOI : 10.1016/j.mbs.2011.06.006

URL : https://hal.archives-ouvertes.fr/hal-00622864

D. 'argenio, D. Park, and K. , Uncertain pharmacokinetic/pharmacodynamic systems: Design, estimation and control, Control Engineering Practice, vol.5, issue.12, pp.1707-1716, 1997.
DOI : 10.1016/S0967-0661(97)10025-9

M. Delattre and M. Lavielle, Coupling the SAEM algorithm and the extended Kalman filter for maximum likelihood estimation in mixed-effects diffusion models, Statistics and Its Interface, vol.6, issue.4
DOI : 10.4310/SII.2013.v6.n4.a10

URL : https://hal.archives-ouvertes.fr/hal-00916803

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Jr. R. Stat. Soc. B, vol.39, pp.1-38, 1977.

S. Ditlevsen, D. Gaetano, and A. , Mixed effects in stochastic differential equation models, REVSTAT Statistical Journal, vol.3, pp.137-153, 2005.

S. Ditlevsen, D. Gaetano, and A. , Stochastic vs. deterministic uptake of dodecanedioic acid by isolated rat livers, Bulletin of Mathematical Biology, vol.67, issue.3, pp.547-561, 2005.
DOI : 10.1016/j.bulm.2004.09.005

S. Ditlevsen and A. Samson, Introduction to Stochastic Models in Biology, Stochastic Biomathematical Models with Applications to the Insulin- Glucose System and Neuronal Modeling, 2012.
DOI : 10.1007/978-3-642-32157-3_1

URL : https://hal.archives-ouvertes.fr/hal-00534445

S. Ditlevsen, K. Yip, and N. Holstein-rathlou, Parameter estimation in a stochastic model of the tubuloglomerular feedback mechanism in a rat nephron, Mathematical Biosciences, vol.194, issue.1, pp.49-69, 2005.
DOI : 10.1016/j.mbs.2004.12.007

S. Ditlevsen, K. Yip, D. J. Marsh, and N. Holstein-rathlou, Parameter estimation of feedback gain in a stochastic model of renal hemodynamics: differences between spontaneously hypertensive and Sprague-Dawley rats, AJP: Renal Physiology, vol.292, issue.2, pp.607-616, 2007.
DOI : 10.1152/ajprenal.00263.2005

S. Donnet, J. Foulley, and A. Samson, Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential Equations, Biometrics, vol.10, issue.3, pp.733-741, 2010.
DOI : 10.1111/j.1541-0420.2009.01342.x

URL : https://hal.archives-ouvertes.fr/hal-00360111

S. Donnet and A. Samson, Parametric inference for mixed models defined by stochastic differential equations, ESAIM: Probability and Statistics, vol.12, pp.196-218, 2008.
DOI : 10.1051/ps:2007045

URL : https://hal.archives-ouvertes.fr/hal-00263515

S. Donnet and A. Samson, EM algorithm coupled with particle filter for maximum likelihood parameter estimation of stochastic differential mixed-effects models, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00519576

A. V. Egorov, H. Li, and Y. Xu, Maximum likelihood estimation of time-inhomogeneous diffusions, Journal of Econometrics, vol.114, issue.1, pp.107-139, 2003.
DOI : 10.1016/S0304-4076(02)00221-X

B. Favetto and A. Samson, Parameter Estimation for a Bidimensional Partially Observed Ornstein-Uhlenbeck Process with Biological Application, Scandinavian Journal of Statistics, vol.11, issue.2, pp.200-220, 2010.
DOI : 10.1111/j.1467-9469.2009.00679.x

URL : https://hal.archives-ouvertes.fr/hal-00324133

L. Ferrante, S. Bompadre, and L. Leone, A Stochastic Compartmental Model with Long Lasting Infusion, Biometrical Journal, vol.45, issue.2, pp.182-194, 2003.
DOI : 10.1002/bimj.200390004

L. Ferrante, S. Bompadre, L. Leone, and M. Montanari, A Stochastic Formulation of the Gompertzian Growth Model forin vitro Bactericidal Kinetics: Parameter Estimation and Extinction Probability, Biometrical Journal, vol.5, issue.3, pp.309-318, 2005.
DOI : 10.1002/bimj.200410125

L. Ferrante, S. Bompadre, L. Possati, and L. Leone, Parameter Estimation in a Gompertzian Stochastic Model for Tumor Growth, Biometrics, vol.41, issue.4, pp.1076-1081, 2000.
DOI : 10.1111/j.0006-341X.2000.01076.x

S. Klim, S. B. Mortensen, N. Kristensen, R. Overgaard, and H. Madsen, Population stochastic modelling (PSM) ? An R package for mixed-effects models based on stochastic differential equations. Computer methods and programs in biomedicine 94, pp.279-289, 2009.

N. Kristensen, H. Madsen, and S. Ingwersen, Using Stochastic Differential Equations for PK/PD Model Development, Journal of Pharmacokinetics and Pharmacodynamics, vol.22, issue.5, pp.109-141, 2005.
DOI : 10.1007/s10928-005-2105-9

E. Kuhn and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models, Computational Statistics & Data Analysis, vol.49, issue.4, pp.1020-1038, 2005.
DOI : 10.1016/j.csda.2004.07.002

M. Lindstrom and D. Bates, Nonlinear Mixed Effects Models for Repeated Measures Data, Biometrics, vol.46, issue.3, pp.673-87, 1990.
DOI : 10.2307/2532087

R. Lipster and A. Shiryaev, Statistics of random processes I : general theory, 2001.

B. Oksendal, Stochastic differential equations: an introduction with applications, 2007.

R. Overgaard, N. Jonsson, C. Tornøe, and H. Madsen, Non-Linear Mixed-Effects Models with Stochastic Differential Equations: Implementation of an Estimation Algorithm, Journal of Pharmacokinetics and Pharmacodynamics, vol.29, issue.5???6, pp.85-107, 2005.
DOI : 10.1007/s10928-005-2104-x

R. V. Overgaard, N. Holford, K. A. Rytved, and H. Madsen, PKPD Model of Interleukin-21 Effects on Thermoregulation in Monkeys???Application and Evaluation of Stochastic Differential Equations, Pharmaceutical Research, vol.23, issue.2, pp.298-309, 2007.
DOI : 10.1007/s11095-006-9143-x

A. Pedersen, A new approach to maximum likelihood estimation for stochastic differential equations based on discrete observations. Scand, J. Statist, vol.22, pp.55-71, 1995.

U. Picchini, D. Gaetano, A. Ditlevsen, and S. , Stochastic differential mixed-effects models. Scand, J. Statist, vol.37, pp.67-90, 2010.

U. Picchini and S. Ditlevsen, Practical estimation of high dimensional stochastic differential mixed-effects models, Computational Statistics & Data Analysis, vol.55, issue.3, pp.1426-1444, 2011.
DOI : 10.1016/j.csda.2010.10.003

U. Picchini, S. Ditlevsen, D. Gaetano, and A. , Modeling the euglycemic hyperinsulinemic clamp by stochastic differential equations, Journal of Mathematical Biology, vol.276, issue.1, pp.771-796, 2006.
DOI : 10.1007/s00285-006-0032-z

U. Picchini, S. Ditlevsen, D. Gaetano, and A. , Maximum likelihood estimation of a time-inhomogeneous stochastic differential model of glucose dynamics, Mathematical Medicine and Biology, vol.25, issue.2, pp.141-155, 2008.
DOI : 10.1093/imammb/dqn011

J. Pinheiro and D. Bates, Mixed-effect models in S and Splus, 2000.

P. Rao and B. , Statistical Inference for Diffusion Type Processes, 1999.

M. Ramanathan, An application of ito's lemma in population pharmacokinetics and pharmacodynamics, Pharmaceutical Research, vol.16, issue.4, pp.584-586, 1999.
DOI : 10.1023/A:1011910800110

M. Ramanathan, A method for estimating pharmacokinetic risks of concentrationdependent drug interactions from preclinical data, Drug Metabolism and disposition, vol.27, pp.1479-1487, 1999.

C. P. Robert and G. Casella, Monte Carlo statistical methods, 2004.

H. Sørensen, Parametric Inference for Diffusion Processes Observed at Discrete Points in Time: a Survey, International Statistical Review, vol.3, issue.3, pp.337-354, 2004.
DOI : 10.1111/j.1751-5823.2004.tb00241.x

C. Tornøe, R. Overgaard, H. Agersø, H. Nielsen, H. Madsen et al., Stochastic Differential Equations in NONMEM??: Implementation, Application, and Comparison with Ordinary Differential Equations, Pharmaceutical Research, vol.31, issue.4, pp.1247-58, 2005.
DOI : 10.1007/s11095-005-5269-5

C. W. Tornøe, J. L. Jacobsen, O. Pedersen, T. Hansen, and H. Madsen, Grey-box Modelling of Pharmacokinetic /Pharmacodynamic Systems, Journal of Pharmacokinetics and Pharmacodynamics, vol.31, issue.5, pp.401-417, 2004.
DOI : 10.1007/s10928-004-8323-8

S. Walker, An EM Algorithm for Nonlinear Random Effects Models, Biometrics, vol.52, issue.3, pp.934-944, 1996.
DOI : 10.2307/2533054