Tang Liansheng Larry, Yuan Ao, Collins John, Che Xuan, Chan Leighton
Department of Statistics, George Mason University, Fairfax, VA, USA.
Rehabilitation Medicine Department, NIH Clinical Center, Bethesda, MD, USA.
Cancer Inform. 2017 Feb 3;16:1176935116686063. doi: 10.1177/1176935116686063. eCollection 2017.
The article proposes a unified least squares method to estimate the receiver operating characteristic (ROC) parameters for continuous and ordinal diagnostic tests, such as cancer biomarkers. The method is based on a linear model framework using the empirically estimated sensitivities and specificities as input "data." It gives consistent estimates for regression and accuracy parameters when the underlying continuous test results are normally distributed after some monotonic transformation. The key difference between the proposed method and the method of Tang and Zhou lies in the response variable. The response variable in the latter is transformed empirical ROC curves at different thresholds. It takes on many values for continuous test results, but few values for ordinal test results. The limited number of values for the response variable makes it impractical for ordinal data. However, the response variable in the proposed method takes on many more distinct values so that the method yields valid estimates for ordinal data. Extensive simulation studies are conducted to investigate and compare the finite sample performance of the proposed method with an existing method, and the method is then used to analyze 2 real cancer diagnostic example as an illustration.
本文提出了一种统一的最小二乘法，用于估计连续和有序诊断测试（如癌症生物标志物）的接收器操作特征（ROC）参数。该方法基于一个线性模型框架，使用经验估计的敏感性和特异性作为输入“数据”。当基础连续测试结果在经过某种单调变换后呈正态分布时，它能给出回归和准确性参数的一致估计。所提出的方法与唐和周的方法之间的关键区别在于响应变量。后者的响应变量是不同阈值下的变换后的经验ROC曲线。对于连续测试结果，它有许多值，但对于有序测试结果，值较少。响应变量的值数量有限使得其对于有序数据不实用。然而，所提出方法中的响应变量有更多不同的值，因此该方法能对有序数据产生有效的估计。进行了广泛的模拟研究，以调查和比较所提出方法与现有方法的有限样本性能，然后使用该方法分析2个真实癌症诊断实例作为说明。
