REFERENCES 1. 2. 3. 4.
5. 6. 9
7. 002 raM8. 72 - 1 9. no10. isre v,3611. 317312. 00-m13. resni14. 15. 16. 17. 18. 19.
Sheiner, L.B. and J. Wakefield, Population modelling in drug development. Statistical Methods in Medical Research, 1999. 8(3): p. 183-193.
Lindstrom, M.L. and D.M. Bates, Nonlinear mixed effects models for repeated measures data. Biometrics, 1990. 46(3): p. 673-87.
Beal, S.L. and L.B. Sheiner, NONMEM users guide. University of California ed. 1992, San Francisco.
Pillai, G.C., F. Mentré, and J.L. Steimer, Non-linear mixed effects modeling - from methodology and software development to driving implementation in drug
development science. Journal of Pharmacokinetics Pharmacodynamics, 2005. 32(2): p. 161-83.
Pinheiro, J.C. and M.D. Bates, Mixed-Effects Models in S and S-Plus. Springer ed. 2000, New-York.
Zhang, L., S.L. Beal, and L.B. Sheiner, Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance. Journal of Pharmacokinetics and Pharmacodynamics, 2003. 30(6): p. 387-404.
Zhang, L., S.L. Beal, and L.B. Sheiner, Simultaneous vs. sequential analysis for population PK/PD data II: robustness of methods. Journal of Pharmacokinetics and Pharmacodynamics, 2003. 30(6): p. 405-416.
Pinheiro, J.C. and D.M. Bates, Approximations to the log-likelihood function in the nonlinear mixed effects models. Journal of Computational and Graphical Statistics, 1995. 4: p. 12-35.
MONOLIX. 2007: Paris.
Samson, A., M. Lavielle, and F. Mentré, Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: application to HIV dynamics model. Computational Statistics and Data Analysis, 2006. 51: p. 1562-1574.
Delyon, B., M. Lavielle, and E. Moulines, Convergence of a stochastic approximation version of the EM algorithm. Annals of Statistics, 1999. 27: p. 94-128.
Khun, E. and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis, 2005. 49: p. 1020-1038.
Al-Banna, M.K., A.W. Kelman, and B. Whiting, Experimental design and efficient parameter estimation in population pharmacokinetics. Journal of Pharmacokinetics and Biopharmaceutics, 1990. 18: p. 347-360.
Jonsson, E.N., J. Wade, and M.O. Karlsson, Comparison of some practical sampling strategies for population pharmacokinetics studies. Journal of Pharmacokinetics and Biopharmaceutics, 1996. 24: p. 245-263.
Atkinson, A.C. and A.N. Donev, Optimum Experimental Designs. Clarendon Press ed. 1992, Oxford.
Walter, E. and L. Pronzato, Identification of Parametric Models from experimental Data. Springer ed. 1997, New-York.
Mentré, F., A. Mallet, and D. Baccar, Optimal design in random effect regression models. Biometrika, 1997. 84(2): p. 429-442.
Retout, S., F. Mentré, and R. Bruno, Fisher information matrix for non-linear mixed-effects models: evaluation and application for optimal design of enoxaparin population pharmacokinetics. Statistics in Medicine, 2002. 21(18): p. 2623-39. Retout, S. and F. Mentré, Further developments of the Fisher information matrix in nonlinear mixed effects models with evaluation in population pharmacokinetics. Journal of Biopharmaceutical Statistics 2003. 13(2): p. 209-27.