DOI QR코드

DOI QR Code

A Statistical Approach to the Pharmacokinetic Model

집단 약동학 모형에 대한 통계학적 고찰

  • Received : 20100200
  • Accepted : 20100400
  • Published : 2010.06.30

Abstract

The Pharmacokinetic model is a complex nonlinear model with pharmacokinetic parameters that is some-times represented by a complex form of differential equations. A population pharmacokinetic model adds individual variability using the random effects to the pharmacokinetic model. It amounts to the nonlinear mixed effect model. This paper, reviews the population pharmacokinetic model from a statistical viewpoint; in addition, a population pharmacokinetic model is also applied to the real clinical data along with a review of the statistical meaning of this model.

약동학 모형은 약동학 모수들의 복잡한 비선형형태의 함수로 복잡한 미분방정식의 형태로 나타나기도 한다. 집단 약동학은 약동학 모형에서 약동학 모수들의 개인 간 차이를 나타내기 위해 이를 랜덤효과로 가정하므로 비선형 혼합 효과 모형이 된다. 본 논문에서는 임상약리학에서 약동학적 특징을 설명하기 위해 사용하는 집단 약동학 모형에 대한 통계학적 고찰을 해 본다. 또한 실제 임상자료를 이용하여 집단 약동학 모형을 적용하여 분석해 봄으로써 통계적 의미를 살펴본다.

Keywords

References

  1. 강주섭, 강주희, 이민호 (2001). <임상약동학의 이해>, 도서출판 신일상사.
  2. 서울대학교 의과대학 (2006). <임상약리학>, 서울대학교 출판부.
  3. Atkinson, A. J., Abernethy, D. R., daniels, C. E., Dedrick, R. L. and Markey, S. P. (2007). Principles of Clinical Pharmacology, Elsevier, San Diego.
  4. Beal, S. B. and Sheiner, L. B. (1979). NONMEM User's Guide, Division of Clinical Pharmacology, University of California, San Francisco.
  5. Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V, NONMEM Project Group, University of California, San Francisco.
  6. Davidian, M. and Giltinan, D. (1995). Nonlinear Models for Repeated Measurement Data, Chapman and Hall, London.
  7. DiPiro, J. T., Spruill, W. J., Wade, W. E., Blouin, R. A. and Pruemer, J. M. (2005). Concepts in Clinical Pharmacokinetics, American Society of Health-System Pharmacists.
  8. Gabrielsson, J. and Weiner, D. (2006). Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications, Apotekarsocieteten, Swedish Academy of Pharmaceutical Sciences.
  9. Gibaldi, M. and Levy, G. (1976). Pharmacokinetics in clinical practice, The Journal of the American Medical Association, 17, 1864-1867.
  10. Harville, D. (1977). Maximum likelihood approaches to variance component estimation and to related problems, Journal of the American Statistical Association, 72, 320-338. https://doi.org/10.2307/2286796
  11. Jonsson, E. N. and Karlsson, M. O. (1999). Xpose-an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM, Computer Methods and Programs in Biomedicine, 58, 51-64. https://doi.org/10.1016/S0169-2607(98)00067-4
  12. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data, Biometrics, 38, 963-974. https://doi.org/10.2307/2529876
  13. Lindstorm, M. J. and Bates, D. M. (1990). Nonlinear mixed effects models for repeated measures data, Biometrics, 46, 673-687. https://doi.org/10.2307/2532087
  14. Racine-Poon, A. (1985). A Bayesian approach to nonlinear random effects models, Biometrics, 41, 1015-1023. https://doi.org/10.2307/2530972
  15. Samson, A., Lavielle, M. and Mentre, F. (2007). The SAEM algorithm for group comparison tests in longitudinal data analysis based on non-linear mixed-effects model, Statistics in Medicine, 26, 4860-4975. https://doi.org/10.1002/sim.2950
  16. Wagner, J. G. (1993). Pharmacokinetics for the Pharmaceutical Scientist, Technomic Publishing Co., Inc., Lancaster.
  17. Wakefiled, J. (1996). The Bayesian analysis of population pharmacokinetic models, Journal of the American Statistican Association, 91, 62-75. https://doi.org/10.2307/2291383

Cited by

  1. Estimation Methods for Population Pharmacokinetic Models using Stochastic Sampling Approach vol.28, pp.2, 2015, https://doi.org/10.5351/KJAS.2015.28.2.175