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The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors
BACKGROUND: The Bland-Altman limits of agreement (LoA) method is almost universally used to compare two measurement methods when the outcome is continuous, despite warnings regarding the often-violated strong underlying statistical assumptions. In settings where only a single measurement per individ...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744297/ https://www.ncbi.nlm.nih.gov/pubmed/36508421 http://dx.doi.org/10.1371/journal.pone.0278915 |
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author | Taffé, Patrick Zuppinger, Claire Burger, Gerrit Marwin Nusslé, Semira Gonseth |
author_facet | Taffé, Patrick Zuppinger, Claire Burger, Gerrit Marwin Nusslé, Semira Gonseth |
author_sort | Taffé, Patrick |
collection | PubMed |
description | BACKGROUND: The Bland-Altman limits of agreement (LoA) method is almost universally used to compare two measurement methods when the outcome is continuous, despite warnings regarding the often-violated strong underlying statistical assumptions. In settings where only a single measurement per individual has been performed and one of the two measurement methods is exempt (or almost) from any measurement error, the LoA method provides biased results, whereas this is not the case for linear regression. METHODS: Thus, our goal is to explain why this happens and illustrate the advantage of linear regression in this particular setting. For our illustration, we used two data sets: a sample of simulated data, where the truth is known, and data from a validation study on the accuracy of a smartphone image-based dietary intake assessment tool. RESULTS: Our results show that when one of the two measurement methods is exempt (or almost) from any measurement errors, the LoA method should not be used as it provides biased results. In contrast, linear regression of the differences on the precise method was unbiased. CONCLUSIONS: The LoA method should be abandoned in favor of linear regression when one of the two measurement methods is exempt (or almost) from measurement errors. |
format | Online Article Text |
id | pubmed-9744297 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-97442972022-12-13 The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors Taffé, Patrick Zuppinger, Claire Burger, Gerrit Marwin Nusslé, Semira Gonseth PLoS One Research Article BACKGROUND: The Bland-Altman limits of agreement (LoA) method is almost universally used to compare two measurement methods when the outcome is continuous, despite warnings regarding the often-violated strong underlying statistical assumptions. In settings where only a single measurement per individual has been performed and one of the two measurement methods is exempt (or almost) from any measurement error, the LoA method provides biased results, whereas this is not the case for linear regression. METHODS: Thus, our goal is to explain why this happens and illustrate the advantage of linear regression in this particular setting. For our illustration, we used two data sets: a sample of simulated data, where the truth is known, and data from a validation study on the accuracy of a smartphone image-based dietary intake assessment tool. RESULTS: Our results show that when one of the two measurement methods is exempt (or almost) from any measurement errors, the LoA method should not be used as it provides biased results. In contrast, linear regression of the differences on the precise method was unbiased. CONCLUSIONS: The LoA method should be abandoned in favor of linear regression when one of the two measurement methods is exempt (or almost) from measurement errors. Public Library of Science 2022-12-12 /pmc/articles/PMC9744297/ /pubmed/36508421 http://dx.doi.org/10.1371/journal.pone.0278915 Text en © 2022 Taffé et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Taffé, Patrick Zuppinger, Claire Burger, Gerrit Marwin Nusslé, Semira Gonseth The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors |
title | The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors |
title_full | The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors |
title_fullStr | The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors |
title_full_unstemmed | The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors |
title_short | The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors |
title_sort | bland-altman method should not be used when one of the two measurement methods has negligible measurement errors |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744297/ https://www.ncbi.nlm.nih.gov/pubmed/36508421 http://dx.doi.org/10.1371/journal.pone.0278915 |
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