this review provides the basic principle and rational for roc analysis of rating and continuous diagnostic test results versus a gold standard. derived indexes of accuracy, in particular area under the curve (auc) has a meaningful interpretation for disease classification from healthy subjects. the methods of estimate of auc and its testing in single diagnostic test and also comparative studies...

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Developing novel nuclear cardiology approaches requires evaluating their diagnostic ability. Statistical measures such as sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC) are useful to evaluate the diagnostic value of novel imaging parameters. This paper reviews key statistical methods used in the evaluation of diagnostic tests and highligh...

Receiver operating characteristic (ROC) curve has been widely used in medical science for its ability to measure the accuracy of diagnostic tests under the gold standard. However, in a complicated medical practice, a gold standard test can be invasive, expensive, and its result may not always be available for all the subjects under study. Thus, a gold standard test is implemented only when it i...

Receiver operating characteristic (ROC) curves evaluate the discriminatory power of a continuous marker to predict a binary outcome. The most popular parametric model for an ROC curve is the binormal model, which assumes that the marker, after a monotone transformation, is normally distributed conditional on the outcome. Here, the authors present an alternative to the binormal model based on th...

Receiver operating characteristic (ROC) curve is widely applied in measuring discriminatory ability of diagnostic or prognostic tests. This makes the ROC analysis one of the most active research areas in medical statistics. Many parametric and semiparametric estimation methods have been proposed for estimating the ROC curve and its functionals. In this paper, we propose the Bayesian bootstrap (...