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 (...

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...

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...

BACKGROUND An explanation is offered for the asymmetry of Receiver Operating Characteristic (ROC) curves obtained from pilots' decisions to eject. The curves can be fitted by a Gaussian model with unequal variances; however, that model does not provide a ready interpretation of the shape of the obtained ROCs. HYPOTHESIS In an emergency, a pilot receives information from many parallel sources ...

Advances in technology provide new diagnostic tests for early detection of disease. Frequently, these tests have continuous outcomes. One popular method to summarize the accuracy of such a test is the Receiver Operating Characteristic (ROC) curve. Methods for estimating ROC curves have long been available. To examine covariate effects, Pepe (1997, 2000) and Alonzo and Pepe (2002) proposed distr...

Receiver operating characteristic (ROC) curves are useful statistical tools used to assess the precision of diagnostic markers or to compare new diagnostic markers with old ones. The most common index employed for these purposes is the area under the ROC curve (theta) and several statistical tests exist that test the null hypotheses H(0): theta= 0.5 or H(0): theta1=theta2, in the case of two-ma...

A Receiver Operating Characteristic (ROC) curve reflects the performance of a system which decides between two competing actions in a test of statistical hypothesis. This paper addresses the inference on ROC curves for the following problem: how can one statistically validate the performance of a system with a claimed ROC curve, ROC0 say? Our proposed solution consists of two main components: F...

Receiver operating characteristic (ROC) curves are used to describe and compare the performance of diagnostic technology and diagnostic algorithms. This paper refines the statistical comparison of the areas under two ROC curves derived from the same set of patients by taking into account the correlation between the areas that is induced by the paired nature of the data. The correspondence betwe...

Receiver operating characteristic (ROC) analysis is widely used to describe the discriminatory power of a diagnostic test to differentiate between populations having or not having a specific disease, using a dichotomous threshold. In this way, positive and negative likelihood ratios (LR+ and LR-) can be calculated to be used in Bayes' way of estimating disease probabilities. Similarly, LRs can ...