Dose-Response Analysis Using R

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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.

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Most cited references 12

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Toward a unified approach to dose-response modeling in ecotoxicology.

Christian Ritz (2009)

This study reviews dose-response models that are used in ecotoxicology. The focus lies on clarification of differences and similarities between models, and as a side effect, their different guises in ecotoxicology are unravelled. A look at frequently used dose-response models reveals major discrepancies, among other things in naming conventions. Therefore, there is a need for a unified view on dose-response modeling in order to improve the understanding of it and to facilitate communication and comparison of findings across studies, thus realizing its full potential. This study attempts to establish a general framework that encompasses most dose-response models that are of interest to ecotoxicologists in practice. The framework includes commonly used models such as the log-logistic and Weibull models, but also features entire suites of models as found in various guidance documents. An outline on how the proposed framework can be implemented in statistical software systems is also provided.

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Improved empirical models describing hormesis.

Jens C. Streibig, Christian Ritz, Nina Cedergreen (2005)

During the past two decades, the phenomenon of hormesis has gained increased recognition. To promote research in hormesis, a sound statistical quantification of important parameters, such as the level and significance of the increase in response and the range of concentration where it occurs, is strongly needed. Here, we present an improved statistical model to describe hormetic dose-response curves and test for the presence of hormesis. Using the delta method and freely available software, any percentage effect dose or concentration can be derived with its associated standard errors. Likewise, the maximal response can be extracted and the growth stimulation calculated. The new model was tested on macrophyte data from multiple-species experiments and on laboratory data of Lemna minor. For the 51 curves tested, significant hormesis was detected in 18 curves, and for another 17 curves, the hormesis model described that data better than the logistic model did. The increase in response ranged from 5 to 109%. The growth stimulation occurred at an average dose somewhere between zero and concentrations corresponding to approximately 20 to 25% of the median effective concentration (EC50). Testing the same data with the hormesis model proposed by Brain and Cousens in 1989, we found no significant hormesis. Consequently, the new model is shown to be far more robust than previous models, both in terms of variation in data and in terms of describing hormetic effects ranging from small effects of a 10% increase in response up to effects of an almost 100% increase in response.

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Linking fluorescence induction curve and biomass in herbicide screening.

Martin Christensen, Harald Teicher, Jens C. Streibig (2003)

A suite of dose-response bioassays with white mustard (Sinapis alba L) and sugar beet (Beta vulgaris L) in the greenhouse and with three herbicides was used to analyse how the fluorescence induction curves (Kautsky curves) were affected by the herbicides. Bentazone, a photosystem II (PSII) inhibitor, completely blocked the normal fluorescence decay after the P-step. In contrast, fluorescence decay was still obvious for flurochloridone, a PDS inhibitor, and glyphosate, an EPSP inhibitor, which indicated that PSII inhibition was incomplete. From the numerous parameters that can be derived from OJIP-steps of the Kautsky curve the relative changes at the J-step [Fvj = (Fm - Fj)/Fm] was selected to be a common response parameter for the herbicides and yielded consistent dose-response relationships. Four hours after treatment, the response Fvj on the doses of bentazone and flurochloridone could be measured. For glyphosate, the changes of the Kautsky curve could similarly be detected 4 h after treatment in sugar beet, but only after 24 hs in S alba. The best prediction of biomass in relation to Fvj was found for bentazone. The experiments were conducted between May and August 2002 and showed that the ambient temperature and solar radiation in the greenhouse could affect dose-response relationships. If the Kautsky curve parameters should be used to predict the outcome of herbicide screening experiments in the greenhouse, where ambient radiation and temperature can only partly be controlled, it is imperative that the chosen fluorescence parameters can be used to predict accurately the resulting biomass used in classical bioassays.

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Author and article information

Contributors

Yinglin Xia: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2015

Publication date (Electronic): 30 December 2015

Volume: 10

Issue: 12

Electronic Location Identifier: e0146021

Affiliations

[1 ]Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 26, DK-1958 Frederiksberg C, Denmark

[2 ]Pneumology, Kantonsspital St. Gallen, Rorschacher Strasse 95, CH-9007 St. Gallen, Switzerland

[3 ]Department of Plant and Environmental Sciences, University of Copenhagen, Højbakkegård Allé 13, DK-2630 Taastrup, Denmark

[4 ]School of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand

University of Rochester, UNITED STATES

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: CR JCS. Performed the experiments: CR JCS. Analyzed the data: CR FB JCS DG. Wrote the paper: CR FB JCS DG.

* E-mail: ritz@ 123456nexs.ku.dk

Article

Publisher ID: PONE-D-15-47606

DOI: 10.1371/journal.pone.0146021

PMC ID: 4696819

PubMed ID: 26717316

SO-VID: aace9a84-c579-4f96-82e8-84c705218057

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

History

Date received : 30 October 2015

Date accepted : 11 December 2015

Page count

Figures: 0, Tables: 1, Pages: 13

Funding

The authors have no support or funding to report.

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Data Availability All relevant data are within the paper and its Supporting Information files.

Dose-Response Analysis Using R

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Abstract

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Most cited references 12

Toward a unified approach to dose-response modeling in ecotoxicology.

Improved empirical models describing hormesis.

Linking fluorescence induction curve and biomass in herbicide screening.

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