Literature DB >> 9408860

Bayesian population pharmacokinetic and pharmacodynamic analyses using mixture models.

G L Rosner1, P Müller.   

Abstract

Population studies of the pharmacokinetics or pharmacodynamics of drugs help us, learn about the variability in drug disposition and effects, information that can be used to treat future patients at safe and effective doses. We present a new approach to population modeling based on a weighted mixture of normal distributions having random weights and means. This method allows estimation of underlying continuous population distributions without prespecifying the parametric form or shape of these probability distributions. Additionally, this method can carry out nonparametric regression of pharmacokinetic or dynamic parameters on patient covariates while estimating the underlying distributions. Two examples illustrate the method and its flexibility.

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Year:  1997        PMID: 9408860     DOI: 10.1023/a:1025784113869

Source DB:  PubMed          Journal:  J Pharmacokinet Biopharm        ISSN: 0090-466X


  18 in total

1.  Building population pharmacokinetic--pharmacodynamic models. I. Models for covariate effects.

Authors:  J W Mandema; D Verotta; L B Sheiner
Journal:  J Pharmacokinet Biopharm       Date:  1992-10

2.  Smooth nonparametric maximum likelihood estimation for population pharmacokinetics, with application to quinidine.

Authors:  M Davidian; A R Gallant
Journal:  J Pharmacokinet Biopharm       Date:  1992-10

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Authors:  M L Lindstrom; D M Bates
Journal:  Biometrics       Date:  1990-09       Impact factor: 2.571

4.  Mixed-effects nonlinear regression for unbalanced repeated measures.

Authors:  E F Vonesh; R L Carter
Journal:  Biometrics       Date:  1992-03       Impact factor: 2.571

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Authors:  L B Sheiner; S Beal; B Rosenberg; V V Marathe
Journal:  Clin Pharmacol Ther       Date:  1979-09       Impact factor: 6.875

6.  Pharmacodynamic analysis of hematologic profiles.

Authors:  G L Rosner; P Müller
Journal:  J Pharmacokinet Biopharm       Date:  1994-12

7.  Alternative approaches to estimation of population pharmacokinetic parameters: comparison with the nonlinear mixed-effect model.

Authors:  J L Steimer; A Mallet; J L Golmard; J F Boisvieux
Journal:  Drug Metab Rev       Date:  1984       Impact factor: 4.518

8.  Bayesian estimation and prediction of clearance in high-dose methotrexate infusions.

Authors:  A Iliadis; M Bachir-Raho; R Bruno; R Favre
Journal:  J Pharmacokinet Biopharm       Date:  1985-02

9.  A Bayesian approach to nonlinear random effects models.

Authors:  A Racine-Poon
Journal:  Biometrics       Date:  1985-12       Impact factor: 2.571

10.  The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods.

Authors:  L B Sheiner
Journal:  Drug Metab Rev       Date:  1984       Impact factor: 4.518

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  6 in total

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Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-11-07       Impact factor: 2.745

2.  Nonlinear Random Effects Mixture Models: Maximum Likelihood Estimation via the EM Algorithm.

Authors:  Xiaoning Wang; Alan Schumitzky; David Z D'Argenio
Journal:  Comput Stat Data Anal       Date:  2007-08-15       Impact factor: 1.681

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4.  Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

Authors:  Alejandro Cruz-Marcelo; Gary L Rosner; Peter Müller; Clinton F Stewart
Journal:  J Stat Theory Pract       Date:  2013-04-01

5.  Two general methods for population pharmacokinetic modeling: non-parametric adaptive grid and non-parametric Bayesian.

Authors:  Tatiana Tatarinova; Michael Neely; Jay Bartroff; Michael van Guilder; Walter Yamada; David Bayard; Roger Jelliffe; Robert Leary; Alyona Chubatiuk; Alan Schumitzky
Journal:  J Pharmacokinet Pharmacodyn       Date:  2013-02-13       Impact factor: 2.745

6.  Population Pharmacokinetic/Pharmacodyanamic Mixture Models via Maximum a Posteriori Estimation.

Authors:  Xiaoning Wang; Alan Schumitzky; David Z D'Argenio
Journal:  Comput Stat Data Anal       Date:  2009-10-01       Impact factor: 1.681

  6 in total

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