Nonparametric Estimation of ROC Curves in the Absence of a Gold Standard

In the evaluation of diagnostic accuracy of tests, a gold standard on the disease status is required. However, in many complex diseases, it is impossible or unethical to obtain such a gold standard. If an imperfect standard is used, the estimated accuracy of the tests would be biased. This type of bias is called imperfect gold standard bias. In this article we develop a nonparametric maximum likelihood method for estimating ROC curves and their areas of ordinal-scale tests in the absence of a gold standard. Our simulation study shows that the proposed estimators for the ROC curve areas have good finite-sample properties in terms of bias and mean squared error. Further simulation studies show that our nonparametric approach is comparable to the binormal parametric method, and is easier to implement. Finally, we illustrate the application of the proposed method in a real clinical study on assessing the accuracy of seven specific pathologists in detecting carcinoma in situ of the uterine cervix.