The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensit...

Disease prevention is important and can be accomplished by developing diagnostic tests. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are used to assess the accuracy of diagnostic tests. The assessment for the superiority between evaluating two diagnostic tests is needed when comparing two diagnostic tests. Existing tests are constructed by comparing t...

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...

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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...

In imaging tasks where the observer is uncertain whether lesions are present, and where they could be present, the image is searched for lesions. In the free-response paradigm, which closely reflects this task, the observer provides data in the form of a variable number of mark-rating pairs per image. In a companion paper a statistical model of visual search has been proposed that has parameter...

The Receiver Operating Characteristic (ROC) curve is a two dimensional measure of classification performance. The area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. In this note, five idealized models are utilized to relate the shape of the ROC curve, and the area under it, to features of the underlying distribution of forecasts. This allows for an interpretati...