RT Journal Article
SR Electronic
T1 Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable: An Example from the Natural History of Unruptured Aneurysms
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
DO 10.3174/ajnr.A2425
A1 O. Naggara
A1 J. Raymond
A1 F. Guilbert
A1 D. Roy
A1 A. Weill
A1 D.G. Altman
YR 2011
UL http://www.ajnr.org/content/early/2011/02/17/ajnr.A2425.abstract
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.