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

Caroline Bennette1 and Andrew Vickers2*

Author Affiliations

1 Pharmaceutical Outcomes Research and Policy Program, University of Washington, Box 357630, Seattle, WA 98195-7630, USA

2 Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 307 E 63 rd St, New York, NY 10065, USA

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BMC Medical Research Methodology 2012, 12:21  doi:10.1186/1471-2288-12-21

Published: 29 February 2012



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.


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.


The use of quantiles is often inadequate for epidemiologic research with continuous variables.