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Evaluation of the Nonparametric Estimation Method in NONMEM VI: Application to Real Data
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)
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: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 36, no 4, 297-315 p.Article in journal (Refereed) Published
Abstract [en]

The aim of the study was to evaluate the nonparametric estimation methods available in NONMEM VI in comparison with the parametric first-order method (FO) and the first-order conditional estimation method (FOCE) when applied to real datasets. Four methods for estimating model parameters and parameter distributions (FO, FOCE, nonparametric preceded by FO (FO-NONP) and nonparametric preceded by FOCE (FOCE-NONP)) were compared for 25 models previously developed using real data and a parametric method. Numerical predictive checks were used to test the appropriateness of each model. Up to 1000 new datasets were simulated from each model and with each method to construct 90% and 50% prediction intervals. The mean absolute error and the mean error of the different outcomes investigated were computed as indicators of imprecision and bias respectively and formal statistical tests were performed. Overall, less imprecision and less bias were observed with nonparametric methods than with parametric methods. Across the 25 models, t-tests revealed that imprecision and bias were significantly lower (P < 0.05) with FOCE-NONP than with FOCE for half of the NPC outcomes investigated. Improvements were even more pronounced with FO-NONP in comparison with FO. In conclusion, when applied to real datasets and evaluated by numerical predictive checks, the nonparametric estimation methods in NONMEM VI performed better than the corresponding parametric methods (FO or FOCE).

Place, publisher, year, edition, pages
2009. Vol. 36, no 4, 297-315 p.
Keyword [en]
Nonparametric estimation method, Numerical predictive check (NPC), Simulation properties, Imprecision, Bias
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-97513DOI: 10.1007/s10928-009-9122-zISI: 000269079600001OAI: oai:DiVA.org:uu-97513DiVA: diva2:172492
Available from: 2008-09-12 Created: 2008-09-12 Last updated: 2017-12-14Bibliographically 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.
Series
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
Keyword
Model building, Absorption model, Transit compartment model, Nonparametric method, Extended grid method, Semiparametric, Distribution transformation, Shrinkage, Model diagnostics
Identifiers
urn:nbn:se:uu:diva-9272 (URN)978-91-554-7275-7 (ISBN)
Public defence
2008-10-03, Room B41, BMC, Uppsala, 09:15
Opponent
Supervisors
Available from: 2008-09-12 Created: 2008-09-12Bibliographically approved
2. Development and Evaluation of Nonparametric Mixed Effects Models
Open this publication in new window or tab >>Development and Evaluation of Nonparametric Mixed Effects Models
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A nonparametric population approach is now accessible to a more comprehensive network of modelers given its recent implementation into the popular NONMEM application, previously limited in scope by standard parametric approaches for the analysis of pharmacokinetic and pharmacodynamic data.

The aim of this thesis was to assess the relative merits and downsides of nonparametric models in a nonlinear mixed effects framework in comparison with a set of parametric models developed in NONMEM based on real datasets and when applied to simple experimental settings, and to develop new diagnostic tools adapted to nonparametric models.

Nonparametric models as implemented in NONMEM VI showed better overall simulation properties and predictive performance than standard parametric models, with significantly less bias and imprecision in outcomes of numerical predictive check (NPC) from 25 real data designs. This evaluation was carried on by a simulation study comparing the relative predictive performance of nonparametric and parametric models across three different validation procedures assessed by NPC. The usefulness of a nonparametric estimation step in diagnosing distributional assumption of parameters was then demonstrated through the development and the application of two bootstrapping techniques aiming to estimate imprecision of nonparametric parameter distributions. Finally, a novel covariate modeling approach intended for nonparametric models was developed with good statistical properties for identification of predictive covariates.

In conclusion, by relaxing the classical normality assumption in the distribution of model parameters and given the set of diagnostic tools developed, the nonparametric approach in NONMEM constitutes an attractive alternative to the routinely used parametric approach and an improvement for efficient data analysis.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. 68 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 136
Keyword
nonparametric, model, pharmacometrics, pharmacokinetics, pharmacodynamic, imprecision, covariate analysis, parameter distribution
National Category
Computer and Information Science Pharmacology and Toxicology
Research subject
Biopharmaceutics; Clinical Pharmacology; Computer Systems; Pharmacology; Statistics
Identifiers
urn:nbn:se:uu:diva-144583 (URN)978-91-554-7995-4 (ISBN)
Public defence
2011-03-22, Room B21, BMC, Husarg. 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2011-03-09 Created: 2011-01-31 Last updated: 2011-05-04Bibliographically approved

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