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

In this paper we describe and discuss three specific estimation procedures that are available within commercially available population software packages. The first version of NONMEM (1) was released in 1979 and later versions are the standard analysis tools in both industry and academia. Recently, two commercially available pieces of software have become available. PPHARM was released during 1994 and POPKAN was released in 1995. We provide descriptions and critique the FOCE method within NONMEM, the two-step algorithm within PPHARM and the Markov chain Monte Carlo method that is utilized by POPKAN. We use simulated data generated from a monoexponential model to evaluate the parameter estimation capabilities of these methods within the three software tools. In particular we investigate the effect on parameter estimation of increasing both interindividual and intraindividual variability.

[1]  Lewis B. Sheiner,et al.  Estimation of population characteristics of pharmacokinetic parameters from routine clinical data , 1977, Journal of Pharmacokinetics and Biopharmaceutics.

[2]  Jon Wakefield,et al.  An Expected Loss Approach to the Design of Dosage Regimens Via Sampling‐Based Methods , 1994 .

[3]  Russell D. Wolfinger,et al.  Laplace's approximation for nonlinear mixed models. , 1993 .

[4]  M Davidian,et al.  Population pharmacokinetic/pharmacodynamic methodology and applications: a bibliography. , 1994, Biometrics.

[5]  J F Boisvieux,et al.  Alternative approaches to estimation of population pharmacokinetic parameters: comparison with the nonlinear mixed-effect model. , 1984, Drug metabolism reviews.

[6]  Stephen G Walker,et al.  AN EM ALGORITHM FOR NONLINEAR RANDOM EFFECTS MODELS , 1996 .

[7]  Nicholas H. G. Holford,et al.  The Population Approach: Rationale, Methods, and Applications in Clinical Pharmacology and Drug Development , 1994 .

[8]  J Wakefield,et al.  A population approach to initial dose selection. , 1997, Statistics in medicine.

[9]  E. Vonesh,et al.  Mixed-effects nonlinear regression for unbalanced repeated measures. , 1992, Biometrics.

[10]  M. Rodier,et al.  Bayesian estimation of p-aminohippurate clearance by a limited sampling strategy. , 1995, Journal of pharmaceutical sciences.

[11]  Lewis B. Sheiner,et al.  Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: Routine clinical pharmacokinetic data , 1983, Journal of Pharmacokinetics and Biopharmaceutics.

[12]  Lewis B. Sheiner,et al.  Evaluation of methods for estimating population pharmacokinetic parameters. I. Michaelis-menten model: Routine clinical pharmacokinetic data , 1980, Journal of Pharmacokinetics and Biopharmaceutics.

[13]  R B Smith,et al.  An evaluation of population pharmacokinetics in therapeutic trials. Part III. Prospective data collection versus retrospective data assembly , 1989, Clinical pharmacology and therapeutics.

[14]  L B Sheiner,et al.  Estimating population kinetics. , 1982, Critical reviews in biomedical engineering.

[15]  Jon Wakefield,et al.  Bayesian individualization via sampling-based methods , 1996, Journal of Pharmacokinetics and Biopharmaceutics.

[16]  D. Bates,et al.  Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data , 1988 .

[17]  E. Vonesh,et al.  A note on the use of Laplace's approximation for nonlinear mixed-effects models , 1996 .

[18]  J. Wakefield The Bayesian Analysis of Population Pharmacokinetic Models , 1996 .

[19]  A. Mallet A maximum likelihood estimation method for random coefficient regression models , 1986 .

[20]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[21]  Donald B. Rubin,et al.  Max-imum Likelihood from Incomplete Data , 1972 .

[22]  Adrian F. M. Smith,et al.  Bayesian Analysis of Linear and Non‐Linear Population Models by Using the Gibbs Sampler , 1994 .

[23]  Marie Davidian,et al.  The Nonlinear Mixed Effects Model with a Smooth Random Effects Density , 1993 .

[24]  R. Gomeni,et al.  A two-step iterative algorithm for estimation in nonlinear mixed-effect models with an evaluation in population pharmacokinetics. , 1995, Journal of biopharmaceutical statistics.

[25]  D. Bates,et al.  Nonlinear mixed effects models for repeated measures data. , 1990, Biometrics.

[26]  Marc Galtier,et al.  A Limited Sampling Model with Bayesian Estimation to Determine Inulin Pharmacokinetics Using the Population Data Modelling Program P-PHARM , 1995 .

[27]  Lewis B. Sheiner,et al.  Evaluation of methods for estimating population pharmacokinetic parameters II. Biexponential model and experimental pharmacokinetic data , 1981, Journal of Pharmacokinetics and Biopharmaceutics.

[28]  L. Tierney,et al.  Accurate Approximations for Posterior Moments and Marginal Densities , 1986 .

[29]  L. Tierney Markov Chains for Exploring Posterior Distributions , 1994 .

[30]  W. Gilks,et al.  Adaptive Rejection Metropolis Sampling Within Gibbs Sampling , 1995 .

[31]  L. Sheiner,et al.  Understanding the Dose-Effect Relationship , 1981, Clinical pharmacokinetics.

[32]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[33]  T. Grasela,et al.  An evaluation of population pharmacokinetics in therapeutic trials. Part I. Comparison of methodologies , 1986, Clinical pharmacology and therapeutics.

[34]  J Wakefield,et al.  An application of Bayesian population pharmacokinetic/pharmacodynamic models to dose recommendation. , 1995, Statistics in medicine.

[35]  A Racine-Poon,et al.  A Bayesian approach to nonlinear random effects models. , 1985, Biometrics.

[36]  S. E. Hills,et al.  Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling , 1990 .

[37]  David J. Spiegelhalter,et al.  Estimation of population pharmacokinetics using the Gibbs sampler , 1995, Journal of Pharmacokinetics and Biopharmaceutics.

[38]  D. Bates,et al.  Approximations to the Log-Likelihood Function in the Nonlinear Mixed-Effects Model , 1995 .

[39]  Gelfand,et al.  AD-A 254 769 BAYESIAN ANALYSIS OF LINEAR AND NONLINEAR POPULATION MODELS USING THE GIBBS SAMPLER , 2022 .