Tang Liansheng Larry, Zhang Wei, Li Qizhai, Ye Xuan, Chan Leighton
Department of Statistics, George Mason University, Fairfax, VA 22030, USA.
Epidemiology and Biostatistics, NIH Clinical Center, Rockville, MD 20814, USA.
Biom J. 2016 Jul;58(4):747-65. doi: 10.1002/bimj.201500099. Epub 2016 Feb 5.
The receiver operating characteristic (ROC) curve is a popular tool to evaluate and compare the accuracy of diagnostic tests to distinguish the diseased group from the nondiseased group when test results from tests are continuous or ordinal. A complicated data setting occurs when multiple tests are measured on abnormal and normal locations from the same subject and the measurements are clustered within the subject. Although least squares regression methods can be used for the estimation of ROC curve from correlated data, how to develop the least squares methods to estimate the ROC curve from the clustered data has not been studied. Also, the statistical properties of the least squares methods under the clustering setting are unknown. In this article, we develop the least squares ROC methods to allow the baseline and link functions to differ, and more importantly, to accommodate clustered data with discrete covariates. The methods can generate smooth ROC curves that satisfy the inherent continuous property of the true underlying curve. The least squares methods are shown to be more efficient than the existing nonparametric ROC methods under appropriate model assumptions in simulation studies. We apply the methods to a real example in the detection of glaucomatous deterioration. We also derive the asymptotic properties of the proposed methods.
当测试结果为连续或有序时，受试者工作特征（ROC）曲线是一种用于评估和比较诊断测试区分患病组和非患病组准确性的常用工具。当对同一受试者的异常和正常部位进行多次测试且测量值在受试者内聚类时，会出现复杂的数据设置情况。尽管最小二乘回归方法可用于从相关数据估计ROC曲线，但如何开发从聚类数据估计ROC曲线的最小二乘方法尚未得到研究。此外，聚类设置下最小二乘方法的统计特性也未知。在本文中，我们开发了最小二乘ROC方法，使基线函数和连接函数有所不同，更重要的是，能够处理具有离散协变量的聚类数据。这些方法可以生成平滑的ROC曲线，满足真实潜在曲线固有的连续性。在模拟研究中，在适当的模型假设下，最小二乘方法比现有的非参数ROC方法更有效。我们将这些方法应用于青光眼病情恶化检测的实际例子中。我们还推导了所提方法的渐近性质。
