PKreport: report generation for checking population pharmacokinetic model assumptions
PKreport: report generation for checking population pharmacokinetic model assumptions
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Author / Creator
Sun, Xiaoyong and Li, Jun
Publisher
England: BioMed Central Ltd
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English
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Publisher
England: BioMed Central Ltd
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Scope and Contents
Contents
Graphics play an important and unique role in population pharmacokinetic (PopPK) model building by exploring hidden structure among data before modeling, evaluating model fit, and validating results after modeling.
The work described in this paper is about a new R package called PKreport, which is able to generate a collection of plots and statistics for testing model assumptions, visualizing data and diagnosing models. The metric system is utilized as the currency for communicating between data sets and the package to generate special-purpose plots. It provides ways to match output from diverse software such as NONMEM, Monolix, R nlme package, etc. The package is implemented with S4 class hierarchy, and offers an efficient way to access the output from NONMEM 7. The final reports take advantage of the web browser as user interface to manage and visualize plots.
PKreport provides 1) a flexible and efficient R class to store and retrieve NONMEM 7 output, 2) automate plots for users to visualize data and models, 3) automatically generated R scripts that are used to create the plots; 4) an archive-oriented management tool for users to store, retrieve and modify figures, 5) high-quality graphs based on the R packages, lattice and ggplot2. The general architecture, running environment and statistical methods can be readily extended with R class hierarchy. PKreport is free to download at http://cran.r-project.org/web/packages/PKreport/index.html....
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Full title
PKreport: report generation for checking population pharmacokinetic model assumptions
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TN_cdi_doaj_primary_oai_doaj_org_article_96e6140a5d314b1b909ba2b008a37ce7
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_96e6140a5d314b1b909ba2b008a37ce7
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ISSN
1472-6947
E-ISSN
1472-6947
DOI
10.1186/1472-6947-11-31