Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems
Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems
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DOI：
10.2307/2670051
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发表时间：
1998-12-01
影响因子：
3.7
通讯作者：
Lloyd, CJ
中科院分区：
文献类型：
--
作者：
Lloyd, CJ
受试者工作特征 (ROC) 曲线用于总结不完善的诊断系统的性能，尤其是在生物医学研究中。这些曲线也适用于总结判别分析的性能，但统计学家未充分利用。本文阐述了如何使用这些曲线来比较竞争诊断系统，开发基于核密度估计的新估计方法，并研究新方法的统计性能。基于“局部人口分离”的思想，进一步提出了ROC曲线的变换。该图形对于显示两个群体之间的差异通常非常有用。该方法应用于包含 7 项诊断结果的数据集，用于预测 353 名患者的癌症活动。这些诊断的分布没有很好地进行参数化建模，因此完全非参数估计或核密度估计似乎都是合适的。 ROC 曲线的构建不仅显示了诊断是否不同，还显示了诊断有何不同。还使用这些数据演示了使用引导模拟来检查和调整平滑偏差。
Receiver operating characteristic (ROC) curves are used for summarizing the performance of imperfect diagnostic systems, especially in biomedical research. These curves are also appropriate for summarizing the performance of a discriminant analysis but are under-utilized by statisticians. This article is illustrates the use of these curves for comparing competing diagnostic systems, develops new estimation methods based on kernel density estimation, and studies the statistical performance of the new method. A transform of the ROC curve is further suggested based on the idea of "local population separation." This graphic is quite generally useful for displaying the differences between two populations. The methods are applied to a dataset comprising the results of seven diagnostics for predicting cancer activity on 353 patients. The distributions of these diagnostics are not well modeled parametrically, and so either completely nonparametric or kernel density estimation seems appropriate. Construction of the ROC curves shows not only if, but also how the diagnostics differ. The use of bootstrap simulation to check and adjust for the bias of smoothing is also demonstrated using these data.