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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
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 |
Sumario: | 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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