A comparison of a Bayesian population method with two methods as implemented in commercially available software

Journal of Pharmacokinetics and Biopharmaceutics

Volumn 24, Issue 4, 1996, Pages 403-432

  Bennett, J E ^a   Wakefield, J C ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b IMPERIAL COLLEGE SCHOOL OF MEDICINE   (United Kingdom)

Author keywords

Mixed effects models; NONMEM; Parameter estimation; POPKAN; Population pharmacokinetics; PPHARM; Simulation

Indexed keywords

ARTICLE; BAYES THEOREM; COMPUTER PROGRAM; INFORMATION PROCESSING; PHARMACOKINETICS; PRIORITY JOURNAL; STATISTICS; THEORETICAL STUDY;

BAYES THEOREM; HUMANS; MATHEMATICAL COMPUTING; MODELS, BIOLOGICAL; PHARMACOKINETICS; SOFTWARE;

EID: 0030514529     PISSN: 0090466X     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02353520     Document Type: Article

References (44)

1. S. L. Beal and L. B. Sheiner. NONMEM User's Guide Version I. Division of Clinical Pharmacology, University of California, San Francisco, 1979.
2. J.-L. Steimer, A. Mallet, J.-L. Golmard, and J.-L. Boisvieux. Alternative approaches to estimation of population pharmacokinetic parameters : Comparison with the nonlinear mixed-effects model. Drug Metab. Rev. 15:265-292 (1984).
3. A. Racine-Poon. A Bayesian approach to nonlinear random effects models. Biometrics 41:1015-1023 (1985).
4. M. J. Lindstrom and D. M. Bates. Nonlinear mixed effects models for repeated measures data. Biometrics 46:673-687 (1990).
5. E. F. Vonesh and R. L. Carter. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48:1-17 (1992).
6. J. C. Wakefield, A. F. M. Smith, A. Racine-Poon, and A. E. Gelfand. Bayesian analysis of linear and nonlinear population models using the Gibbs sampler. Appl. Statist. 41:201-221 (1994).
7. A. Mallet. A maximum likelihood estimation method for random coefficient regression models. Biometrika 73:645-656 (1986).
8. M. Davidian and A. R. Gallant. The nonlinear mixed effects model with a smooth random effects density. Biometrika 80:475-488 (1993).
9. M. Davidian and D. M. Giltinan. Nonlinear Models for Repeated Measures Data, Chapman and Hall, 1995.
10. L. Yuh, S. Beal, M. Davidian, F. Harrison, A. Hester, K. Kowalski, E. Vonesh, and R. Wolfinger. Population pharmacokinetic/pharmacodynamic methodology and applications: A bibliography. Biometrics 50:566-575 (1994).
11. L. B. Sheiner and S. L. Beal. Evaluation of methods for estimating population pharmacokinetic parameters. I. Michaelis-Menten model: Routine clinical pharmacokinetic data. J. Pharmacokin. Biopharm. 8:533-571 (1980).
12. L. B. Sheiner and S. L. Beal. Evaluation of methods for estimating pharmacokinetic parameters. II. Biexponential model and experimental pharmacokinetic data. J. Pharmacokin. Biopharm. 9:635-651 (1981).
13. L. B. Sheiner and S. L. Beal. Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: Routine clinical pharmacokinetic data. J. Pharmacokin. Biopharm. 11:303-319 (1983).
14. T. H. Grasela, E. J. Antal, R. J. Townsend, and R. B. Smith. An evaluation of population pharmacokinetics in therapeutic trials. Part I. Comparison of methodologies. Clin. Pharmacol. Ther. 39:605-612 (1986).
15. A. Racine-Poon and A. F. M. Smith. Population models. In D. Berry (ed.), Statistical Methodology in the Pharmaceutical Sciences, Marcel-Dekker, New York, 1990, pp. 139-162.
16. S. L. Beal and L. B. Sheiner. Estimating population kinetics. CRC Crit. Rev. Biomed. Eng. 8:195-222 (1982).
17. L. B. Sheiner, B. Rosenburg, and V. V. Marathe. Estimation of population characteristics of pharmacokinetic parameters from routine clinical data. J. Pharmacokin. Biopharm. 5:445-479 (1977).
18. N. M. Laird and J. H. Ware. Random-effects models for longitudinal data. Biometrics 38:963-974 (1982).
19. M. J. Lindstrom and D. M. Bates. Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. J. Am. Statist. Assoc. 83:1014-1022 (1988).
20. S. L. Beal and L. B. Sheiner. NONMEM User's Guide Version IV, Part VII. Conditional Estimation Methods, University of California, San Francisco, 1992.
21. J. C. Wakefield and S. G. Walker. A Bayesian population approach to initial dose selection. Statist. Med. (in press).
22. N. H. G. Holford and L. B. Sheiner. Understanding the dose-effect relationship. Clin. Pharmacokin. 6:429-453 (1981).
23. J. C. Pinheiro and D. M. Bates. Approximations to the loglikelihood function in the nonlinear mixed effects model. J. Comput. Graph. Statist. (1995).
24. R. Wolfinger. Laplace's approximation for nonlinear mixed models. Biometrika 80:791-795 (1993).
25. E. F. Vonesh. A note on the Laplace's approximation for nonlinear mixed-effects models. Biometrika 83:447-452 (1996).
26. L. Tierney and J. B. Kadane. Accurate approximations for posterior moments and marginal densities. J. Am. Statist. Assoc. 81:82-86 (1986).
27. J.-L. Steimer, S. Vozeh, A. Racine, N. G. Holford, and R. O'Neill. The population approach: Rationale, methods and applications in clinical pharmacology and drug development. In Handbook of Experimental Pharmacology, Vol. 110. Pharmacokinetics of Drugs, Springer-Verlag, Heidelberg, 1994.
28. F. Mentré and G. Gomeni. A two step iterative algorithm for estimation in nonlinear mixed effect models with an evaluation in population pharmacokinetics. J. Biopharm. Statist. 5:141-158 (1995).
29. G. Prévost. Estimation of a normal probability density function from samples measured with non-negligible and non-constant dispersion. Internal Report, Andersa-Gerbtos, 2 avenue du ler mai, F-91120 Palaiseau, 1977.
30. J. M. Kinowski, M. Rodier, F. Bressolle, D. Fabre, V. Augey, J. L. Richard, M. Galtier, and R. Gomeni. Bayesian estimation of p-aminohippurate clearance by a limited sampling strategy. J. Pharm. Sci. 84:307-311 (1995).
31. J. M. Kinowski, F. Bressolle, M. Rodier, V. Augey, D. Fabre, J. L. Richard, M. Galtier, and R. Gomeni. A limited sampling model with Bayesian estimation to determine insulin pharmacokinetics using the population data modelling program P-PHARM. Clin. Pharmacokin. 9:260-269 (1995).
32. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. Roy. Statist. Soc. B 39:1-38 (1977).
33. S. G. Walker. An EM algorithm for nonlinear random effects models. Biometrics 52:934-944 (1996).
34. A. E. Gelfand, S. E. Hills, A. Racine-Poon, and A. F. M. Smith. Illustration of Bayesian inference in normal data models using Gibbs sampling. J. Am. Statist. Assoc. 85:972-985 (1990).
35. L. Tierney. Markov chains for exploring posterior distributions. Ann. Statist. 22:1701-1762 (1994).
36. J. E. Bennett, A. Racine-Poon, and J. C. Wakefield. Markov chain Monte Carlo for nonlinear hierarchical models. In W. R. Gilks, S. Richardson, and D. J. Spiegelhalter (eds.). Markov Chain Monte Carlo in Practice, Chapman and Hall, London, 1995.
37. J. C. Wakefield. An expected loss approach to the design of dosage regimens via sampling-based methods. Statistician 43:13-29 (1994).
38. J. C. Wakefield. Bayesian individualization via a sampling-based methods. J. Pharmacokin. Biopharm. 24:103-131 (1996).
39. J. C. Wakefield. The Bayesian analysis of population pharmacokinetic models. J. Am. Statist. Assoc. 91:62-75 (1966).
40. J. C. Wakefield and A. Racine-Poon. An application of Bayesian population pharmacokinetic/pharmacodynamic models to dose recommendation. Statist. Med. 14:971-986 (1995).
41. A. Racine-Poon and J. C. Wakefield. Bayesian analysis of population pharmacokinetic and instantaneous pharmacodynamic relationships. In D. Berry and D. Stangl (eds.), Bayesian Biostatistics, Marcel Dekker, New York, 1996.
42. W. R. Gilks, N. G. Best, and K. K. C. Tan. Adaptive Rejection Metropolis sampling within Gibbs sampling. Appl. Statist. 44:455-472 (1995).
43. N. G. Best, K. K. C. Tan, W. R. Gilks, and D. J. Spiegelhalter. Estimation of population pharmacokinetics using the Gibbs sampler. J. Pharmacokin. Biopharm. 23:407-424 (1995).
44. PPHARM. User's manual, SIMED Scientific Software Development, Créteil, France, 1994.