In diagnostic studies, researchers frequently encounter imperfect reference standards with some misclassified labels. Treating these as gold standards can bias receiver operating characteristic (ROC) curve analysis. To address this issue, we propose a novel likelihood-based method under a nonparametric density ratio model. This approach enables the reliable estimation of the ROC curve, area under the curve (AUC), partial AUC, and Youden's index with favorable statistical properties. To implement the method, we develop an efficient expectation-maximization algorithm algorithm. Extensive simulations evaluate its finite-sample performance, showing smaller mean squared errors in estimating the ROC curve, partial AUC, and Youden's index compared to existing methods. We apply the proposed approach to a malaria study.

翻译：在诊断学研究中，研究者常会遇到带有误分类标签的不完善参考标准。若将其视为金标准，会导致受试者工作特征（ROC）曲线分析产生偏倚。为解决此问题，我们提出了一种基于密度比模型的非参数似然方法。该方法能够可靠地估计ROC曲线、曲线下面积（AUC）、部分AUC以及约登指数，并具有良好的统计性质。为实现该方法，我们开发了一种高效的期望最大化算法。大量模拟实验评估了其有限样本性能，结果表明在估计ROC曲线、部分AUC和约登指数时，其均方误差较现有方法更小。我们将所提方法应用于一项疟疾研究。