Streiner David L, Cairney John
Kunin-Lunenfeld Applied Research Unit, Baycrest Centre, Toronto, Ontario.
Can J Psychiatry. 2007 Feb;52(2):121-8. doi: 10.1177/070674370705200210.
It is often necessary to dichotomize a continuous scale to separate respondents into normal and abnormal groups. However, because the distributions of the scores in these 2 groups most often overlap, any cut point that is chosen will result in 2 types of errors: false negatives (that is, abnormal cases judged to be normal) and false positives (that is, normal cases placed in the abnormal group). Changing the cut point will alter the numbers of erroneous judgments but will not eliminate the problem. A technique called receiver operating characteristic (ROC) curves allows us to determine the ability ofa test to discriminate between groups, to choose the optimal cut point, and to compare the performance of 2 or more tests. We discuss how to calculate and compareROC curves and the factors that must be considered in choosing an optimal cut point.
通常有必要将连续量表进行二分法划分，以便将受访者分为正常组和异常组。然而，由于这两组分数的分布大多存在重叠，因此所选择的任何切点都会导致两种错误：假阴性（即被判定为正常的异常病例）和假阳性（即被归入异常组的正常病例）。改变切点会改变错误判断的数量，但无法消除这个问题。一种称为受试者工作特征（ROC）曲线的技术使我们能够确定一项测试区分不同组别的能力、选择最佳切点，并比较两项或更多项测试的性能。我们将讨论如何计算和比较ROC曲线，以及在选择最佳切点时必须考虑的因素。
