Constructing "Proper'' ROCs from Ordinal Response Data Using Weighted Power Functions
Constructing "Proper'' ROCs from Ordinal Response Data Using Weighted Power Functions
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DOI：
10.1177/0272989x13503046
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发表时间：
2014-05-01
影响因子：
3.6
通讯作者：
Peng, Hongying
中科院分区：
文献类型：
--
作者：
Mossman, Douglas;Peng, Hongying
背景受试者工作特征（ROC）分析是描述诊断系统准确性的标准方法，其中决策任务涉及区分2种互斥的可能性。目前流行的副标准曲线拟合模型通常产生不适当的ROC，因为它们不具有信号检测理论所要求的不断减小的斜率。并非罕见，副正态ROC有可见的钩，错误地暗示比机会更差的诊断分化，其中曲线位于无信息对角线下方。在这篇文章中，我们提出并评估了一个2参数加权幂函数（WPF）模型，该模型总是产生一个具有正的单调递减斜率的适当ROC曲线。方法.我们使用了计算机模拟研究，比较副正态和WPF模型的结果。结果WPF模型产生的ROC曲线比使用副正态模型获得的曲线偏差更小，更接近真实值。WPF模型的更好的性能来自其设计约束，作为一个必要的适当的ROC。结论. WPF模型比以前发布的幂函数模型拟合更广泛的数据集，同时保持原始决策变量，特定操作点，ROC曲线轮廓和模型参数之间的直接关系。与其他合适的ROC模型相比，WPF模型的特点是简单，避免了传统副正态ROC模型的缺陷。
Background. Receiver operating characteristic (ROC) analysis is the standard method for describing the accuracy of diagnostic systems where the decision task involves distinguishing between 2 mutually exclusive possibilities. The popular binormal curve-fitting model usually produces ROCs that are improper in that they do not have the ever-decreasing slope required by signal detection theory. Not infrequently, binormal ROCs have visible hooks that falsely imply worse-than-chance diagnostic differentiation where the curve lies below the no-information diagonal. In this article, we present and evaluate a 2-parameter, weighted power function (WPF) model that always results in a proper ROC curve with a positive, monotonically decreasing slope. Methods. We used a computer simulation study to compare results from binormal and WPF models. Results. The WPF model produces ROC curves that are less biased and closer to the true values than are curves obtained using the binormal model. The better performance of the WPF model follows from its design constraint as a necessarily proper ROC. Conclusions. The WPF model fits a broader variety of data sets than previously published power function models while maintaining straightforward relationships among the original decision variable, specific operating points, ROC curve contours, and model parameters. Compared with other proper ROC models, the WPF model is distinctive in its simplicity, and it avoids the flaws of the conventional binormal ROC model.