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Evaluation of an Extended Grid Method for Estimation Using Nonparametric Distributions
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)
2009 (English)In: AAPS Journal, ISSN 1550-7416, Vol. 11, no 3, 615-627 p.Article in journal (Refereed) Published
Abstract [en]

A nonparametric population method with support points from the   empirical Bayes estimates (EBE) has recently been introduced (default   method). However, EBE distribution may, with sparse and small datasets,   not provide a suitable range of support points. This study aims to   develop a method based on a prior parametric analysis capable of   providing a nonparametric grid with adequate support points range. A   new method extends the nonparametric grid with additional support   points generated by simulation from the parametric distribution, hence   the name extended-grid method. The joint probability density function   is estimated at the extended grid. The performance of the new method   was evaluated and compared to the default method via Monte Carlo   simulations using simple IV bolus model and sparse (200 subject, two   samples per subject) or small (30 subjects, three samples per subjects)   datasets and two scenarios based on real case studies. Parameter   distributions estimated by the default and the extended-grid method   were compared to the true distributions; bias and precision were   assessed at different percentiles. With small datasets, the bias was   similar between methods (< 10%); however, precision was markedly   improved with the new method (by 43%). With sparse datasets, both bias   (from 5.9% to 3%) and precision (by 60%) were improved. For simulated   scenarios based on real study designs, extended-grid predictions were   in a good agreement with true values. A new approach to obtain support   points for the nonparametric method has been developed, and it   displayed good estimation properties. The extended-grid method is   automated, using the program PsN, for implementation into the NONMEM.

Place, publisher, year, edition, pages
2009. Vol. 11, no 3, 615-627 p.
Keyword [en]
empirical Bayes estimates, extended grid method, NONMEM, nonparametric method, parameter distribution
National Category
Pharmaceutical Sciences
URN: urn:nbn:se:uu:diva-97514DOI: 10.1208/s12248-009-9138-8ISI: 000270544500023OAI: oai:DiVA.org:uu-97514DiVA: diva2:172493
Available from: 2008-09-12 Created: 2008-09-12 Last updated: 2010-07-06Bibliographically approved
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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