PT  - JOURNAL ARTICLE
AU  - O. Naggara
AU  - J. Raymond
AU  - F. Guilbert
AU  - D. Roy
AU  - A. Weill
AU  - D.G. Altman
TI  - Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable: An Example from the Natural History of Unruptured Aneurysms
AID  - 10.3174/ajnr.A2425
DP  - 2011 Feb 16
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2011/02/17/ajnr.A2425.short
4100  - http://www.ajnr.org/content/early/2011/02/17/ajnr.A2425.full
AB  - SUMMARY: 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.