Tosteson A N, Begg C B
Division of Biostatistics and Epidemiology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Med Decis Making. 1988 Jul-Sep;8(3):204-15. doi: 10.1177/0272989X8800800309.
A method for applying generalized ordinal regression models to categorical rating data to estimate and analyze receiver operating characteristic (ROC) curves is presented. These models permit parsimonious adjustment of ROC curve parameters for relevant covariates through two regression equations that correspond to location and scale. Particular shapes of ROC curves are interpreted in relation to the kind of covariates included in the two regressions. The model is shown to be flexible because it is not restricted to the assumption of binormality that is commonly employed in smoothed ROC curve estimation, although the binormal model is one particular form of the more general model. The new method provides a mechanism for pinpointing the effect that interobserver variability has on the ROC curve. It also allows for the adjustment of ROC curves for temporal variation and case mix, and provides a way to assess the incremental diagnostic value of a test. The new methodology is recommended because it substantially improves the ability to assess diagnostic tests using ROC curves.
本文提出了一种将广义有序回归模型应用于分类评级数据以估计和分析受试者工作特征（ROC）曲线的方法。这些模型通过两个分别对应位置和尺度的回归方程，允许对相关协变量的ROC曲线参数进行简约调整。根据两个回归中包含的协变量类型来解释ROC曲线的特定形状。该模型具有灵活性，因为它不限于平滑ROC曲线估计中常用的双正态假设，尽管双正态模型是更一般模型的一种特殊形式。新方法提供了一种机制，用于确定观察者间变异性对ROC曲线的影响。它还允许针对时间变化和病例组合对ROC曲线进行调整，并提供了一种评估测试增量诊断价值的方法。推荐使用这种新方法，因为它大大提高了使用ROC曲线评估诊断测试的能力。
