Measures including sensitivity, specificity, and positive and negative predictive values have been traditionally used to assess a diagnostic test's ability to detect the presence or absence of disease. Receiver operating characteristic (ROC) curve analysis allows visual evaluation of the trade-offs between sensitivity and specificity associated with different values of the test result, or diffe...

Receiver operating characteristic (ROC) analysis was introduced to the field of eyewitness identification 5 years ago. Since that time, it has been both influential and controversial, and the debate has raised an issue about measuring discriminability that is rarely considered. The issue concerns the distinction between empirical discriminability (measured by area under the ROC curve) vs. under...

The receiver operating characteristics (ROC) curve is a standard statistical tool to characterize the accuracy of diagnostic tests when test results are continuous. It provides a complete description of test performance and a meaningful way to compare the performances of different tests. The empirical (nonparametric) ROC curve is the most popular estimator of the ROC curve. Semiparametric estim...

The receiver operating characteristic (ROC) curve, the positive predictive value (PPV) curve and the negative predictive value (NPV) curve are three measures of performance for a continuous diagnostic biomarker. The ROC, PPV and NPV curves are often estimated empirically to avoid assumptions about the distributional form of the biomarkers. Recently, there has been a push to incorporate group se...

Uplift modeling is a classification method that determines the incremental impact of an action on a given population. Uplift modeling aims at maximizing the area under the uplift curve, which is the difference between the subject and control sets’ area under the lift curve. Lift and uplift curves are seldom used outside of the marketing domain, whereas the related ROC curve is frequently used i...

Receiver Operator Characteristic (ROC) curves are commonly applied as metrics for quantifying the performance of binary fault detection systems. An ROC curve provides a visual representation of a detection system’s True Positive Rate versus False Positive Rate sensitivity as the detection threshold is varied. The area under the curve provides a measure of fault detection performance independent...

RATIONALE AND OBJECTIVES Receiver operating characteristic (ROC) curves are ubiquitous in the analysis of imaging metrics as markers of both diagnosis and prognosis. While empirical estimation of ROC curves remains the most popular method, there are several reasons to consider smooth estimates based on a parametric model. MATERIALS AND METHODS A mixture model is considered for modeling the di...

We introduce the Skill Plot, a method that it is directly relevant to a decision maker who must use a diagnostic test. In contrast to ROC curves, the skill curve allows easy graphical inspection of the optimal cutoff or decision rule for a diagnostic test. The skill curve and test also determine whether diagnoses based on this cutoff improve upon a naive forecast (of always present or of always...

Receiver Operating Characteristic (ROC) analysis has been successfully applied to classifier problems with two classes. The Area Under the ROC Curve (AUC) has been elected as a better way to evaluate classifiers than predictive accuracy or error and has also recently used for evaluating probability estimators. However, the extension of the Area Under the ROC Curve for more than two classes has ...

We identified plasma biomarkers that presaged outcomes in patients with gastrointestinal graft-versus-host disease (GVHD) by measuring 23 biomarkers in samples collected before initiation of treatment. Six analytes with the greatest accuracy in predicting grade 3-4 GVHD in the first cohort (74 patients) were then tested in a second cohort (76 patients). The same 6 analytes were also tested in s...