Overestimation of the receiver operating characteristic curve for logistic regression
Overestimation of the receiver operating characteristic curve for logistic regression
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DOI：
10.1093/biomet/89.2.315
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发表时间：
2002-06-01
期刊：
影响因子：
2.7
通讯作者：
Corbett, P
中科院分区：
文献类型：
--
作者：
Copas, JB;Corbett, P
Logistic回归经常被用来寻找协变量的线性组合，从而最好地区分两组或两个群体。ROC(接收者操作特征)曲线是评估结果分数的性能的好方法，但使用相同的数据来拟合分数和计算其ROC会导致对如果在未来病例的样本上验证该分数将给出的性能的过度乐观的估计。本文研究了这种高估的程度，并建议对ROC曲线本身和曲线下面积进行收缩修正。这种修正与Efron关于二元预测规则误差率偏差的公式是一致的。文中讨论了两个医学实例。
Logistic regression is often used to find a linear combination of covariates which best discriminates between two groups or populations. The ROC, receiver operating characteristic, curve is a good way of assessing the performance of the resulting score, but using the same data both to fit the score and to calculate its ROC leads to an over-optimistic estimate of the performance which the score would give if it were to be validated on a sample of future cases. The paper studies the extent of this overestimation, and suggests a shrinkage correction for the ROC curve itself and for the area under the curve. The correction is consistent with Efron's formula for the bias in the error rate of a binary prediction rule. Two medical examples are discussed.