Use and misuse of the receiver operating characteristic curve in risk prediction
Use and misuse of the receiver operating characteristic curve in risk prediction
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DOI：
10.1161/circulationaha.106.672402
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发表时间：
2007-02-20
期刊：
影响因子：
37.8
通讯作者：
Cook, Nancy R.
中科院分区：
文献类型：
--
作者：
Cook, Nancy R.
c统计量，或受试者工作特征（ROC）曲线下面积，在诊断测试中获得了普及，其中灵敏度和特异性的测试特征与区分患病与非患病患者有关。然而，c统计量在评估预测未来风险或将个体划分为风险类别的模型时可能不是最佳的。在这种情况下，校准对于准确评估风险同样重要。例如，比值比为3的生物标志物可能对c统计量的影响很小，但增加的水平可能会使个体患者的10年心血管风险估计值从8%变为24%，这将导致根据当前成人治疗组III指南的不同治疗建议。公认的危险因素如血脂、高血压和吸烟对c统计量的影响很小，但可以更准确地将大部分患者重新分类为高风险或低风险类别。事实上，复杂疾病的完美校准模型只能达到远低于理论最大值1的c统计量值。因此，使用c统计量进行模型选择可能会天真地从心血管风险预测评分中消除已确定的风险因素。随着新的风险因素的发现，仅仅依靠c统计量来评估它们作为风险预测因子的效用似乎是不明智的。
The c statistic, or area under the receiver operating characteristic ( ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10- year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher- risk or lower- risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.