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Receiver Operating Characteristic (ROC) analysis is one of the most widely used methods for summarizing intrinsic properties of a diagnostic system, and is often used in evaluation and comparison of diagnostic technologies, practices or systems. These methods play an important role in public health since they enable researchers to achieve a greater insight into the properties of diagnostic test...

The justification for making a measurement can be sought in asking what decisions are based on measurement, such as assessing the compliance of quality characteristic an entity relation to specification limit, SL. relative performance testing devices and classification algorithms used is often evaluated using venerable ever popular receiver operating (ROC). However, ROC tool has potentially all...

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...

OBJECTIVE This was an exploratory analysis to develop a new way of representing BILAG-2004 system scores longitudinally that would be clinically meaningful and easier to analyse in comparison with multiple categorical variables. METHODS Data from a multicentre longitudinal study of SLE patients (the BILAG-2004 index and therapy collected at every visit) were used. External responsiveness anal...

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...

The receiver operating characteristic (ROC) curve is used for classification between two populations. Usually two independent random samples from the populations with absolutely continuous cumulative distribution functions (CDF) F and G are considered. Then the ROC curve is defined as ROC(t) = 1−G(F−1(1− t)) for 0 < t < 1 if the inverse F−1 exists. Classical ROC curve estimator is based on empi...

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 ...