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.

Highlights

  • Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis 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 doseresponse analyses in general

  • We have described the key functionality presently available in the R extension package drc

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Summary

RESEARCH ARTICLE

Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis 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 doseresponse 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

Introduction
Types of responses
Modelling the mean
Model type
Estimation procedures
Nonlinear least squares
Maximum likelihood estimation
Robust estimation
Constrained estimation
Self starter functions
Choice of model
Obtaining relevant parameter estimates
Inverse regression
Findings
Discussion
Full Text
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