期刊
AMERICAN JOURNAL OF NEURORADIOLOGY
卷 32, 期 3, 页码 437-440出版社
AMER SOC NEURORADIOLOGY
DOI: 10.3174/ajnr.A2425
关键词
-
资金
- Canadian Institutes of Health Research [NCT00537134]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据