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AJNR Am J Neuroradiol. 2011 Mar;32(3):437-40. doi: 10.3174/ajnr.A2425. Epub 2011 Feb 17.

Analysis by categorizing or dichotomizing continuous variables is inadvisable: an example from the natural history of unruptured aneurysms.

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  • 1Department of Radiology, Interventional Neuroradiology Research Unit, International Consortium of Neuroendovascular Centres, University of Montreal, CHUM Notre-Dame Hospital, Quebec, Canada.


In medical research analyses, continuous variables are often converted into categoric variables by grouping values into ≥2 categories. The simplicity achieved by creating ≥2 artificial groups has a cost: Grouping may create rather than avoid problems. In particular, dichotomization leads to a considerable loss of power and incomplete correction for confounding factors. The use of data-derived "optimal" cut-points can lead to serious bias and should at least be tested on independent observations to assess their validity. Both problems are illustrated by the way the results of a registry on unruptured intracranial aneurysms are commonly used. Extreme caution should restrict the application of such results to clinical decision-making. Categorization of continuous data, especially dichotomization, is unnecessary for statistical analysis. Continuous explanatory variables should be left alone in statistical models.

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