Receiver operating characteristic (ROC) curves play a central role in the evaluation of biomarkers and tests for disease diagnosis. Predictors for event time outcomes can also be evaluated with ROC curves, but the time lag between marker measurement and event time must be acknowledged. We discuss different definitions of time-dependent ROC curves in the context of real applications. Several app...

There are various methods to estimate the parameters in the binormal model for the ROC curve. In this paper, we propose a conceptually simple and computationally accessible Bayesian estimation method using a partial likelihood based on ranks. Posterior consistency is also established. We compare the new method with other estimation methods and conclude that our estimator generally performs bett...

In imaging tasks where the observer is uncertain whether lesions are present, and where they could be present, the image is searched for lesions. In the free-response paradigm, which closely reflects this task, the observer provides data in the form of a variable number of mark-rating pairs per image. In a companion paper a statistical model of visual search has been proposed that has parameter...

OBJECTIVES To evaluate the responsiveness of generic and mapped preference-based measures based on the anchor of global change in health condition of colorectal cancer (CRC) patients. STUDY DESIGN AND SETTING A baseline sample of 333 Chinese CRC patients was recruited between September 2009 and July 2010 and was surveyed prospectively at 6-month follow-up. Preference-based indices were derive...

The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Non-parametric, semiparametric and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under...

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...

In many practical classiication problems it is important to distinguish false positive from false negative results when evaluating the performance of the classiier. This is of particular importance for medical diagnostic tests. In this context, receiver operating characteristic (ROC) curves have become a standard tool. Here we apply this concept to characterize the performance of a simple neura...

Eyewitness identification is a pivotal issue in applied research because, in practice, a correct identification can help to remove a dangerous criminal from society, but a false identification can lead to the erroneous conviction of an innocent suspect. Consequently, psychologists have tried to ascertain the best procedures for collecting identification evidence, evaluating them using measures ...

If we consider the Brier score (B) in the context of the signal detection theory and assume that it makes sense to consider the existence of B as a parameter for the population (let B be this B), and if we assume that the calibration in the observer's probability estimate is perfect, we find that there is a theoretical relationship between B and the area under the binormal receiver operating ch...

The receiver operating characteristic (ROC) curve is used to describe the performance of a diagnostic test which classifies observations into two groups. We introduce a new method for selecting bandwidths when computing kernel estimates of ROC curves. Our technique allows for interaction between the distributions of each group of observations and gives substantial improvement in MISE over other...