Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents

BACKGROUND: Quantiles are a staple of epidemiologic research: in contemporary epidemiologic practice, continuous variables are typically categorized into tertiles, quartiles and quintiles as a means to illustrate the relationship between a continuous exposure and a binary outcome. DISCUSSION: In thi...

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Detalles Bibliográficos
Autores principales: Bennette, Caroline, Vickers, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Debate
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3353173/
https://www.ncbi.nlm.nih.gov/pubmed/22375553
http://dx.doi.org/10.1186/1471-2288-12-21
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author Bennette, Caroline
Vickers, Andrew
author_facet Bennette, Caroline
Vickers, Andrew
author_sort Bennette, Caroline
collection PubMed
description BACKGROUND: Quantiles are a staple of epidemiologic research: in contemporary epidemiologic practice, continuous variables are typically categorized into tertiles, quartiles and quintiles as a means to illustrate the relationship between a continuous exposure and a binary outcome. DISCUSSION: In this paper we argue that this approach is highly problematic and present several potential alternatives. We also discuss the perceived drawbacks of these newer statistical methods and the possible reasons for their slow adoption by epidemiologists. SUMMARY: The use of quantiles is often inadequate for epidemiologic research with continuous variables.
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spelling pubmed-33531732012-05-16 Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents Bennette, Caroline Vickers, Andrew BMC Med Res Methodol Debate BACKGROUND: Quantiles are a staple of epidemiologic research: in contemporary epidemiologic practice, continuous variables are typically categorized into tertiles, quartiles and quintiles as a means to illustrate the relationship between a continuous exposure and a binary outcome. DISCUSSION: In this paper we argue that this approach is highly problematic and present several potential alternatives. We also discuss the perceived drawbacks of these newer statistical methods and the possible reasons for their slow adoption by epidemiologists. SUMMARY: The use of quantiles is often inadequate for epidemiologic research with continuous variables. BioMed Central 2012-02-29 /pmc/articles/PMC3353173/ /pubmed/22375553 http://dx.doi.org/10.1186/1471-2288-12-21 Text en Copyright ©2012 Bennette and Vickers; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Debate
Bennette, Caroline
Vickers, Andrew
Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents
title Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents
title_full Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents
title_fullStr Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents
title_full_unstemmed Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents
title_short Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents
title_sort against quantiles: categorization of continuous variables in epidemiologic research, and its discontents
topic Debate
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3353173/
https://www.ncbi.nlm.nih.gov/pubmed/22375553
http://dx.doi.org/10.1186/1471-2288-12-21
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