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

Journal article

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 observatio...

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Authors: Naggara, O; Raymond, J; Guilbert, F; Roy, D; Weill, A

• Publication status: Published
• Journal: AJNR. American journal of neuroradiology
• Volume: 32
• Issue: 3
• Pages: 437-440
• Publication date: 2011-03-01
• DOI: 10.3174/ajnr.a2425
• EISSN: 1936-959X
• ISSN: 0195-6108
• Language: English
• Keywords: Risk Factors; Aneurysm, Ruptured; Reproducibility of Results; Data Interpretation, Statistical; Sensitivity and Specificity; Proportional Hazards Models; Risk Assessment; Humans; Intracranial Aneurysm; Bias (Epidemiology); Prevalence