Hillis Stephen L
The University of Iowa.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12929. doi: 10.1117/12.3008642. Epub 2024 Mar 29.
Because the conventional binormal ROC curve parameters are in terms of the underlying normal diseased and nondiseased rating distributions, transformations of these values are required for the user to understand what the corresponding ROC curve looks like in terms of its shape and size. In this paper I propose an alternative parameterization in terms of parameters that explicitly describe the shape and size of the ROC curve. The proposed two parameters are the mean-to-sigma ratio and the familiar area under the ROC curve (AUC), which are easily interpreted in terms of the shape and size of the ROC curve, respectively. In addition, the mean-to-sigma ratio describes the degree of improperness of the ROC curve and the AUC describes the ability of the corresponding diagnostic test to discriminate between diseased and nondiseased cases. The proposed parameterization simplifies the sizing of diagnostic studies when conjectured variance components are used and simplifies choosing the binormal and parameter values needed for simulation studies.
由于传统的副法线ROC曲线参数是基于潜在的正态患病和非患病评级分布，因此用户需要对这些值进行变换，才能了解相应的ROC曲线在形状和大小方面是什么样的。在本文中，我提出了一种基于明确描述ROC曲线形状和大小的参数的替代参数化方法。所提出的两个参数是均值与标准差之比以及熟悉的ROC曲线下面积（AUC），它们分别可以根据ROC曲线的形状和大小轻松解释。此外，均值与标准差之比描述了ROC曲线的不合适程度，而AUC描述了相应诊断测试区分患病和非患病病例的能力。当使用推测的方差分量时，所提出的参数化简化了诊断研究的规模确定，并简化了选择模拟研究所需的双正态和参数值的过程。
