A study on parametric approaches to compare areas under two correlated ROC curves

Nonparametric Methods in Comparing Two Correlated Roc Curves

Receiver Operating Characteristic (ROC) analysis is one of the most widely used methods for summarizing intrinsic properties of a diagnostic system, and is often used in evaluation and comparison of diagnostic technologies, practices or systems. These methods play an important role in public health since they enable researchers to achieve a greater insight into the properties of diagnostic test...

Regression Analysis for Correlated ROC Curves

Novel tests for evaluating two ROC curves under paired samples.

Disease prevention is important and can be accomplished by developing diagnostic tests. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are used to assess the accuracy of diagnostic tests. The assessment for the superiority between evaluating two diagnostic tests is needed when comparing two diagnostic tests. Existing tests are constructed by comparing t...

Compare diagnostic tests using transformation-invariant smoothed ROC curves().

Receiver operating characteristic (ROC) curve, plotting true positive rates against false positive rates as threshold varies, is an important tool for evaluating biomarkers in diagnostic medicine studies. By definition, ROC curve is monotone increasing from 0 to 1 and is invariant to any monotone transformation of test results. And it is often a curve with certain level of smoothness when test ...

Jackknife Empirical Likelihood Based Confidence Intervals for Partial Areas Under ROC Curves

The partial area under the ROC curve (partial AUC) summarizes the accuracy of a diagnostic or screening test over a relevant region of the ROC curve and represents a useful tool for the evaluation and the comparison of tests. In this paper, we propose a jackknife empirical likelihood method for making inference on partial AUCs. Following the idea in Jing, Yuan, and Zhou (2009), we combine the e...