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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.

Original publication




Journal article


AJNR Am J Neuroradiol

Publication Date





437 - 440


Aneurysm, Ruptured, Bias, Data Interpretation, Statistical, Humans, Intracranial Aneurysm, Prevalence, Proportional Hazards Models, Reproducibility of Results, Risk Assessment, Risk Factors, Sensitivity and Specificity