Against quantiles: Categorization of continuous variables in epidemiologic research, and its discontents Journal Article
| Authors: | Bennette, C.; Vickers, A. |
| Article Title: | Against quantiles: Categorization of continuous variables in epidemiologic research, and its discontents |
| 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. © 2012 Bennette and Vickers; licensee BioMed Central Ltd. |
| Keywords: | patient selection; risk factors; risk factor; models, statistical; sampling studies; epidemiology; bias (epidemiology); statistical model; mass communication; diffusion of innovation; epidemiologic research design |
| Journal Title: | BMC Medical Research Methodology |
| Volume: | 12 |
| ISSN: | 1471-2288 |
| Publisher: | Biomed Central Ltd |
| Date Published: | 2012-02-29 |
| Start Page: | 21 |
| Language: | English |
| DOI: | 10.1186/1471-2288-12-21 |
| PROVIDER: | scopus |
| PMCID: | PMC3353173 |
| PUBMED: | 22375553 |
| DOI/URL: | |
| Notes: | --- - "Export Date: 30 August 2012" - "Source: Scopus" |
