Browse > Article
http://dx.doi.org/10.5351/KJAS.2015.28.2.175

Estimation Methods for Population Pharmacokinetic Models using Stochastic Sampling Approach  

Kim, Kwang-Hee (Department of Statistics, Ewha Womans University)
Yoon, Jeong-Hwa (Department of Statistics, Ewha Womans University)
Lee, Eun-Kyung (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.2, 2015 , pp. 175-188 More about this Journal
Abstract
This study is about estimation methods for the population pharmacokinetic and pharmacodymic model. This is a nonlinear mixed effect model, and it is difficult to find estimates of parameters because of nonlinearity. In this study, we examined theoretical background of various estimation methods provided by NONMEM, which is the most widely used software in the pharmacometrics area. We focused on estimation methods using a stochastic sampling approach - IMP, IMPMAP, SAEM and BAYES. The SAEM method showed the best performance among methods, and IMPMAP and BAYES methods showed slightly less performance than SAEM. The major obstacle to a stochastic sampling approach is the running time to find solution. We propose new approach to find more precise initial values using an ITS method to shorten the running time.
Keywords
Nonlinear mixed effect model; stochastic sampling; maximum likelihood estimation method; EM algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Bauer, R. J. (2010). NONMEM 7 Technical Guide, Icon Development Solution, Ellicott City, Maryland.
2 Beal, S. L., Sheiner, L. B., Boeckmann, A. and Bauer, R. J. (1992). NONMEM Users Guides, NONMEM Project Group, University of California, San Francisco.
3 Celeux, G. and Diebolt, J. (1992). A stochastic approximation type EM algorithm for the mixture problem, Stochastics: An International Journal of Probability and Stochastic Processes, 41, 119-134.   DOI
4 Delyon, B., Lavielle, M. and Moulines, E. (1999). Convergence of a stochastic approximation version of the EM algorithm, Annals of Statistics, 94-128.
5 Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological), 1-38.
6 Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice, Chapman Hall/CRC, New York.
7 Kuhn, E. and Lavielle, M. (2005). Maximum likelihood estimation in nonlinear mixed effects models, Computational Statistics and Data Analysis, 49, 1020-1038.   DOI   ScienceOn
8 Lee, E.-K. (2010). A statistical approach to the pharmacokinetic model, The Korean Journal of Applied Statistics, 23, 511-520.   DOI   ScienceOn
9 Mentre, F. (2008). Stochastic EM algorithms in population pharmacokinetic-pharmacodynamic analyses, In The American Conference on Pharmacometrics (ACoP), 9-12.
10 Robert, C. and Casella, G. (2013). Monte Carlo Statistical Methods, Springer Science and Business Media.
11 Wang, Y. (2007). Derivation of various NONMEM estimation methods, Journal of Pharmacokinetics and Pharmacodynamics, 34, 575-593.   DOI