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Dose-Response Analysis Using R

Author

Listed:
  • Christian Ritz
  • Florent Baty
  • Jens C Streibig
  • Daniel Gerhard

Abstract

Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.

Suggested Citation

  • Christian Ritz & Florent Baty & Jens C Streibig & Daniel Gerhard, 2015. "Dose-Response Analysis Using R," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0146021
    DOI: 10.1371/journal.pone.0146021
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    References listed on IDEAS

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    1. Kahm, Matthias & Hasenbrink, Guido & Lichtenberg-Fraté, Hella & Ludwig, Jost & Kschischo, Maik, 2010. "grofit: Fitting Biological Growth Curves with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i07).
    2. Ritz, Christian & Streibig, Jens C., 2005. "Bioassay Analysis Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i05).
    3. Baty, Florent & Ritz, Christian & Charles, Sandrine & Brutsche, Martin & Flandrois, Jean-Pierre & Delignette-Muller, Marie-Laure, 2015. "A Toolbox for Nonlinear Regression in R: The Package nlstools," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i05).
    4. Bornkamp, Björn & Pinheiro, José & Bretz, Frank, 2009. "MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i07).
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