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A simplified method to approximate a ROC curve with a Bézier curve to calculate likelihood ratios of quantitative test results
In order to calculate likeli hood ratios (LR) values for quantitative test results, a distribution-independent algorithm based on Bézier curves is proposed. Receiver operating characteristic (ROC) analysis provides the LR as the slope of the tangent to the ROC curve at the point corresponding to the...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Elsevier
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298645/ https://www.ncbi.nlm.nih.gov/pubmed/32566488 http://dx.doi.org/10.1016/j.mex.2020.100915 |
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author | Fierz, Walter |
author_facet | Fierz, Walter |
author_sort | Fierz, Walter |
collection | PubMed |
description | In order to calculate likeli hood ratios (LR) values for quantitative test results, a distribution-independent algorithm based on Bézier curves is proposed. Receiver operating characteristic (ROC) analysis provides the LR as the slope of the tangent to the ROC curve at the point corresponding to the test result. • Here, we make use of cubic Bézier curves defined by Bernstein polynomials of degree 3. • A simplified method to adjust a Bézier curve to a ROC curve is presented • The crucial advantage of this procedure is that Bézier curves are constructed by tangents to the ROC curve, whose slopes immediately provide the LR of a specific point on the curve. |
format | Online Article Text |
id | pubmed-7298645 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-72986452020-06-19 A simplified method to approximate a ROC curve with a Bézier curve to calculate likelihood ratios of quantitative test results Fierz, Walter MethodsX Biochemistry, Genetics and Molecular Biology In order to calculate likeli hood ratios (LR) values for quantitative test results, a distribution-independent algorithm based on Bézier curves is proposed. Receiver operating characteristic (ROC) analysis provides the LR as the slope of the tangent to the ROC curve at the point corresponding to the test result. • Here, we make use of cubic Bézier curves defined by Bernstein polynomials of degree 3. • A simplified method to adjust a Bézier curve to a ROC curve is presented • The crucial advantage of this procedure is that Bézier curves are constructed by tangents to the ROC curve, whose slopes immediately provide the LR of a specific point on the curve. Elsevier 2020-05-16 /pmc/articles/PMC7298645/ /pubmed/32566488 http://dx.doi.org/10.1016/j.mex.2020.100915 Text en © 2020 The Author(s). Published by Elsevier B.V. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Biochemistry, Genetics and Molecular Biology Fierz, Walter A simplified method to approximate a ROC curve with a Bézier curve to calculate likelihood ratios of quantitative test results |
title | A simplified method to approximate a ROC curve with a Bézier curve to calculate likelihood ratios of quantitative test results |
title_full | A simplified method to approximate a ROC curve with a Bézier curve to calculate likelihood ratios of quantitative test results |
title_fullStr | A simplified method to approximate a ROC curve with a Bézier curve to calculate likelihood ratios of quantitative test results |
title_full_unstemmed | A simplified method to approximate a ROC curve with a Bézier curve to calculate likelihood ratios of quantitative test results |
title_short | A simplified method to approximate a ROC curve with a Bézier curve to calculate likelihood ratios of quantitative test results |
title_sort | simplified method to approximate a roc curve with a bézier curve to calculate likelihood ratios of quantitative test results |
topic | Biochemistry, Genetics and Molecular Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298645/ https://www.ncbi.nlm.nih.gov/pubmed/32566488 http://dx.doi.org/10.1016/j.mex.2020.100915 |
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