Huang Xin, Qin Gengsheng, Fang Yixin
Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia 30303, USA.
Biometrics. 2011 Jun;67(2):568-76. doi: 10.1111/j.1541-0420.2010.01450.x. Epub 2010 Jun 16.
When several diagnostic tests are available, one can combine them to achieve better diagnostic accuracy. This article considers the optimal linear combination that maximizes the area under the receiver operating characteristic curve (AUC); the estimates of the combination's coefficients can be obtained via a nonparametric procedure. However, for estimating the AUC associated with the estimated coefficients, the apparent estimation by re-substitution is too optimistic. To adjust for the upward bias, several methods are proposed. Among them the cross-validation approach is especially advocated, and an approximated cross-validation is developed to reduce the computational cost. Furthermore, these proposed methods can be applied for variable selection to select important diagnostic tests. The proposed methods are examined through simulation studies and applications to three real examples.
当有多种诊断测试可用时，可以将它们组合起来以获得更好的诊断准确性。本文考虑了使接收器操作特征曲线（AUC）下面积最大化的最优线性组合；组合系数的估计可以通过非参数方法获得。然而，对于估计与估计系数相关的AUC，通过重新代入进行的表观估计过于乐观。为了调整向上的偏差，提出了几种方法。其中特别提倡交叉验证方法，并开发了一种近似交叉验证以降低计算成本。此外，这些提出的方法可用于变量选择以选择重要的诊断测试。通过模拟研究和对三个实际例子的应用来检验所提出的方法。
