Ma Shuangge, Huang Jian
Division of Biostatistics, Yale University, New Haven, Connecticut 06520, USA.
Biometrics. 2007 Sep;63(3):751-7. doi: 10.1111/j.1541-0420.2006.00731.x.
In biomedical studies, it is of great interest to develop methodologies for combining multiple markers for the purpose of disease classification. The receiving operating characteristic (ROC) technique has been widely used, where classification performance can be measured with the area under the ROC curve (AUC). In this article, we study a ROC-based method for effectively combining multiple markers for disease classification. We propose a sigmoid AUC (SAUC) estimator that maximizes the sigmoid approximation of the empirical AUC. The SAUC estimator is computationally affordable, n(1/2)-consistent and achieves the same asymptotic efficiency as the AUC estimator. Inference based on the weighted bootstrap is investigated. We also propose Monte Carlo methods to assess the overall prediction performance and the relative importance of individual markers. Finite sample performance is evaluated using simulation studies and two public data sets.
在生物医学研究中，开发用于组合多个标记物以进行疾病分类的方法具有重大意义。接收者操作特征（ROC）技术已被广泛使用，其中分类性能可以通过ROC曲线下面积（AUC）来衡量。在本文中，我们研究了一种基于ROC的有效组合多个标记物进行疾病分类的方法。我们提出了一种sigmoid AUC（SAUC）估计器，它使经验AUC的sigmoid近似最大化。SAUC估计器计算成本低，具有n(1/2)一致性，并且与AUC估计器具有相同的渐近效率。研究了基于加权自助法的推断。我们还提出了蒙特卡罗方法来评估整体预测性能和单个标记物的相对重要性。使用模拟研究和两个公共数据集评估有限样本性能。
