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

BMC Med Res Methodol. 2012 Feb 29;12:21. doi: 10.1186/1471-2288-12-21.


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.

MeSH terms

  • Bias*
  • Diffusion of Innovation*
  • Epidemiologic Research Design*
  • Humans
  • Models, Statistical*
  • Patient Selection
  • Risk Factors
  • Sampling Studies