ROC curve regression analysis: the use of ordinal regression models for diagnostic test assessment.
نویسندگان
چکیده
منابع مشابه
ROC curve regression analysis: the use of ordinal regression models for diagnostic test assessment.
Diagnostic tests commonly are characterized by their true positive (sensitivity) and true negative (specificity) classification rates, which rely on a single decision threshold to classify a test result as positive. A more complete description of test accuracy is given by the receiver operating characteristic (ROC) curve, a graph of the false positive and true positive rates obtained as the dec...
متن کاملROC analysis in ordinal regression learning
Nowadays the area under the receiver operating characteristics (ROC) curve, which corresponds to the Wilcoxon–Mann–Whitney test statistic, is increasingly used as a performance measure for binary classification systems. In this article we present a natural generalization of this concept for more than two ordered categories, a setting known as ordinal regression. Our extension of the Wilcoxon–Ma...
متن کاملRegression Analysis for the Partial Area under the Roc Curve
Performance evaluation of any classification method is fundamental to its acceptance in practice. Evaluation should consider the dependence of a classifier’s accuracy on relevant covariates in addition to its overall accuracy. When developing a classifier with a continuous output that allocates units into one of two groups, receiver operating characteristic (ROC) curve analysis is appropriate. ...
متن کاملReceiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation
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...
متن کاملSUGI 27: Use of the ROC Curve and the Bootstrap in Comparing Weighted Logistic Regression Models
In analyzing data from a survey, researchers often need to compare the effectiveness of several logistic regression models. The receiver operating characteristic curve offers one way to measure effectiveness of prediction, by calculating the area under the curve (AUC). We present a SAS macro for calculating AUC that takes the survey weights into account. For comparing logistic regression models...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Environmental Health Perspectives
سال: 1994
ISSN: 0091-6765,1552-9924
DOI: 10.1289/ehp.94102s873