Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable: An Example from the Natural History of Unruptured Aneurysms

作者：Naggara O; Raymond J^*; Guilbert F; Roy D; Weill A; Altman D G

来源：American Journal of Neuroradiology, 2011, 32(3): 437-440.

DOI：10.3174/ajnr.A2425

In medical research analyses, continuous variables are often converted into categoric variables by grouping values into categories. The simplicity achieved by creating 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.

• 出版日期2011-3