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Evaluation of the Nonparametric Estimation Method in NONMEM VI
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)ORCID iD: 0000-0003-3531-9452
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
2009 (English)In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 37, no 1, 27-35 p.Article in journal (Refereed) Published
Abstract [en]

PURPOSE: In NONMEM VI, a novel method exists for estimation of a nonparametric parameter distribution. The parameter distributions are approximated by discrete probability density functions at a number of parameter values (support points). The support points are obtained from the empirical Bayes estimates from a preceding parametric estimation step, run with the First Order (FO) or First Order Conditional Estimation (FOCE) methods. The purpose of this work is to evaluate this new method with respect to parameter distribution estimation. METHODS: The performance of the method, with special emphasis on the analysis of data with non-normal distribution of random effects, was studied using Monte Carlo (MC) simulations. RESULTS: The mean value (and ranges) of absolute relative biases (ARBs, %) in parameter distribution estimates with nonparametric methods preceded with FO and FOCE were 0.80 (0.1-3.7) and 0.70 (0-3), respectively, while for parametric methods, these values were 23.74 (3.3-97.5) and 4.38 (0.1-17.9), for FO and FOCE, respectively. The nonparametric estimation method in NONMEM could identify non-normal parameter distributions and correct bias in parameter estimates seen when applying the FO estimation method. CONCLUSIONS: The method shows promising properties when analyzing different types of pharmacokinetic (PK) data with both the FO and FOCE methods as preceding steps.

Place, publisher, year, edition, pages
2009. Vol. 37, no 1, 27-35 p.
Keyword [en]
Nonparametric estimation method, NONMEM, Parameter distribution estimation, Pharmacokinetics
National Category
Pharmaceutical Sciences
URN: urn:nbn:se:uu:diva-97512DOI: 10.1016/j.ejps.2008.12.014ISI: 000264704400003OAI: oai:DiVA.org:uu-97512DiVA: diva2:172491
Available from: 2008-09-12 Created: 2008-09-12 Last updated: 2015-01-23
In thesis
1. Improved pharmacometric model building techniques
Open this publication in new window or tab >>Improved pharmacometric model building techniques
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Pharmacometric modelling is an increasingly used method for analysing the outcome from clinical trials in drug development. The model building process is complex and involves testing, evaluating and diagnosing a range of plausible models aiming to make an adequate inference from the observed data and predictions for future studies and therapy.

The aim of this thesis was to advance the approaches used in pharmacometrics by introducing improved models and methods for application in essential parts of model building procedure: (i) structural model development, (ii) stochastic model development and (iii) model diagnostics.

As a contribution to the structural model development, a novel flexible structural model for drug absorption, a transit compartment model, was introduced and evaluated. This model is capable of describing various drug absorption profiles and yet simple enough to be estimable from data available from a typical trial. As a contribution to the stochastic model development, three novel methods for parameter distribution estimation were developed and evaluated; a default NONMEM nonparametric method, an extended grid method and a semiparametric method with estimated shape parameters. All these methods are useful in circumstances when standard assumptions of parameter distributions in the population do not hold. The new methods provide less biased parameter estimates, better description of variability and better simulation properties of the model. As a contribution to model diagnostics, the most commonly used diagnostics were evaluated for their usefulness. In particular, diagnostics based on individual parameter estimates were systematically investigated and circumstances which are likely to misguide modelers towards making erroneous decisions in model development, relating to choice of structural, covariate and stochastic model components were identified.

In conclusion, novel approaches, insights and models have been provided to the pharmacometrics community.

Implementation of these advances to make model building more efficient and robust has been facilitated by development of diagnostic tools and automated routines.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 98 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 80Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 80
Model building, Absorption model, Transit compartment model, Nonparametric method, Extended grid method, Semiparametric, Distribution transformation, Shrinkage, Model diagnostics
urn:nbn:se:uu:diva-9272 (URN)978-91-554-7275-7 (ISBN)
Public defence
2008-10-03, Room B41, BMC, Uppsala, 09:15
Available from: 2008-09-12 Created: 2008-09-12Bibliographically approved

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Kjellsson, Maria C.Karlsson, Mats O.
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