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Perl-speaks-NONMEM (PsN) – a Perl module for NONMEM related programming
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
2004 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 75, no 2, 85-94 p.Article in journal (Refereed) Published
Abstract [en]

The NONMEM program is the most widely used nonlinear regression software in population pharmacokinetic/pharmacodynamic (PK/PD) analyses. In this article we describe a programming library, Perl-speaks-NONMEM (PsN), intended for programmers that aim at using the computational capability of NONMEM in external applications. The library is object oriented and written in the programming language Perl. The classes of the library are built around NONMEM's data, model and output files. The specification of the NONMEM model is easily set or changed through the model and data file classes while the output from a model fit is accessed through the output file class. The classes have methods that help the programmer perform common repetitive tasks, e.g. summarising the output from a NONMEM run, setting the initial estimates of a model based on a previous run or truncating values over a certain threshold in the data file. PsN creates a basis for the development of high-level software using NONMEM as the regression tool.

Place, publisher, year, edition, pages
2004. Vol. 75, no 2, 85-94 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-94377DOI: 10.1016/j.cmpb.2003.11.003PubMedID: 15212851OAI: oai:DiVA.org:uu-94377DiVA: diva2:168207
Available from: 2006-04-21 Created: 2006-04-21 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Development, Application and Evaluation of Statistical Tools in Pharmacometric Data Analysis
Open this publication in new window or tab >>Development, Application and Evaluation of Statistical Tools in Pharmacometric Data Analysis
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Pharmacometrics uses models based on pharmacology, physiology and disease for quantitative analysis of interactions between drugs and patients. The availability of software implementing modern statistical methods is important for efficient model building and evaluation throughout pharmacometric data analyses.

The aim of this thesis was to facilitate the practical use of available and new statistical methods in the area of pharmacometric data analysis. This involved the development of suitable software tools that allows for efficient use of these methods, characterisation of basic properties and demonstration of their usefulness when applied to real world data. The thesis describes the implementation of a set of statistical methods (the bootstrap, jackknife, case-deletion diagnostics, log-likelihood profiling and stepwise covariate model building), made available as tools through the software Perl-speaks-NONMEM (PsN). The appropriateness of the methods and the consistency of the software tools were evaluated using a large selection of clinical and nonclinical data. Criteria based on clinical relevance were found to be useful components in automated stepwise covariate model building. Their ability to restrict the number of included parameter-covariate relationships while maintaining the predictive performance of the model was demonstrated using the antiarrythmic drug dofetilide. Log-likelihood profiling was shown to be equivalent to the bootstrap for calculating confidence intervals for fixed-effects parameters if an appropriate estimation method is used. The condition number of the covariance matrix for the parameter estimates was shown to be a good indicator of how well resampling methods behave when applied to pharmacometric data analyses using NONMEM. The software developed in this thesis equips modellers with an enhanced set of tools for efficient pharmacometric data analysis.

Place, publisher, year, edition, pages
Uppsala: Avdelningen för farmakokinetik och läkemedelsterapi, 2006. 46 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 33
Keyword
Pharmaceutical biosciences, pharmacometrics, pharmacokinetics, pharmacodynamics, methodology, statistics, model evaluation, resampling methods, Farmaceutisk biovetenskap
Identifiers
urn:nbn:se:uu:diva-6825 (URN)91-554-6544-7 (ISBN)
Public defence
2006-05-12, B22, BMC, Husargatan 3, Uppsala, 09:15
Opponent
Supervisors
Available from: 2006-04-21 Created: 2006-04-21Bibliographically approved

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