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
SP 437
OP 440
DO 10.3174/ajnr.A2425
VO 32
IS 3
A1 Naggara, O.
A1 Raymond, J.
A1 Guilbert, F.
A1 Roy, D.
A1 Weill, A.
A1 Altman, D.G.
YR 2011
UL http://www.ajnr.org/content/32/3/437.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. ACAanterior cerebral arteryCHUMCentre hospitalier de l'Université de MontréalICAinternal carotid arteryISUIAInternational Study of Unruptured Intracranial AneurysmsMCAmiddle cerebral arteryPcircposterior circulationPcomAposterior communicating arterySAHsubarachnoid hemorrhageUIAunruptured intracranial aneurysms