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 ...

Receiver operating characteristic (ROC) analysis is a widely accepted method for analyzing and comparing the diagnostic accuracy of radiological tests. In this paper we will explain the basic principles underlying ROC analysis and provide practical information on the use and interpretation of ROC curves. The major applications of ROC analysis will be discussed and their limitations will be addr...

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...

.................................................................................................................VIII

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

Precision-recall (PR) and receiver operating characteristic (ROC) curves are valuable measures of classifier performance. Here, we present the R-package PRROC, which allows for computing and visualizing both PR and ROC curves. In contrast to available R-packages, PRROC allows for computing PR and ROC curves and areas under these curves for soft-labeled data using a continuous interpolation betw...

Studies of diagnostic accuracy require more sophisticated methods for their meta-analysis than studies of therapeutic interventions. A number of different, and apparently divergent, methods for meta-analysis of diagnostic studies have been proposed, including two alternative approaches that are statistically rigorous and allow for between-study variability: the hierarchical summary receiver ope...

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...