BACKGROUND Teenage pregnancy is a known social problem which has been previously described using a number of deprivation measures. This study aimed to explore the temporal patterns of teenage pregnancy in Aberdeen, Scotland and to assess the discriminating ability of three measures of socioeconomic status. METHODS This was a population-based study from 1950 to 2010, using data from the Aberde...

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...

quantity is the area under the so-called receiver operating characteristic (ROC) curve, 1 AMS 2000 subject classifications. Primary: 62G99; Secondary: 62H30, 62G20.

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 ...

Diagnostic tests are central in the field of modern medicine. One of the main factors for interpreting a diagnostic test is the discriminatory accuracy. For a continuous-scale diagnostic test, the area under the receiver operating characteristic (ROC) curve, AUC, is a useful onenumber summary index for the diagnostic accuracy of the test. When only a particular region of the ROC curve would be ...

The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the cu...

The purpose of this tutorial-based lecture is to show the usefulness of performing a receiver operating characteristic (ROC) curve analysis. The lecture will explain the background terminology associated with a ROC curve analysis, show the necessary SAS software coding to run a ROC curve analysis, and finally help interpret the analysis output in order to make informed research decisions. An ex...