Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable: An Example from the Natural History of Unruptured Aneurysms
Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable: An Example from the Natural History of Unruptured Aneurysms
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DOI：
10.3174/ajnr.a2425
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发表时间：
2011-03-01
影响因子：
3.5
通讯作者：
Altman, D. G.
中科院分区：
文献类型：
--
作者：
Naggara, O.;Raymond, J.;Altman, D. G.
在医学研究分析中，通常通过将值分组来将连续变量转换为类别变量。通过创建人工分组来实现简单性是有代价的：分组可能会产生而不是避免问题。特别是，二分法会导致显着的功效损失和对混杂因素的不完全校正。使用数据得出的“最佳”切点可能会导致严重的偏差，至少应该通过独立观察进行测试，以评估其有效性。这两个问题可以通过未破裂颅内动脉瘤登记结果的常用方式来说明。应极其谨慎地限制此类结果在临床决策中的应用。连续数据的分类，尤其是二分法，对于统计分析来说是不必要的，连续解释变量应该在统计模型中单独保留。
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.