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. 2012 Feb 29:12:21.
doi: 10.1186/1471-2288-12-21.

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

Caroline Bennette  1 , Andrew Vickers
Affiliations

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

Caroline Bennette et al. BMC Med Res Methodol. .

Abstract

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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Figures

Figure 1
Figure 1
The risk of prostate cancer by level of PSA, with the distribution of PSA levels (ng/ml) among a population-based sample. The shading in the distribution gives the quartiles of PSA.
Figure 2
Figure 2
Prostate cancer risk by PSA (black dashed line), with predicted risks using either cubic splines (light gray solid line) or quartiles (dark gray solid line).
Figure 3
Figure 3
The learning curve for cancer control after radical prostatectomy.
See this image and copyright information in PMC

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