PDF ROC (Receiver Operating Characteristic) Curve Analysis
ROC (Receiver Operating Characteristic) Curve Analysis
Julie Xu
17th November 2017
Agenda
Introduction Definition Accuracy Application Conclusion Reference
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Introduction
ROC (Receiver Operating Characteristic) curve is a fundamental tool for diagnostic test evaluation. It is increasingly used in many fields, such as data mining, financial credit scoring, weather forecasting etc.
ROC curve plots the true positive rate (sensitivity) of a test versus its false positive rate (1-specificity) for different cut-off points of a parameter
ROC curve is graphically to display the trade-off relationship between sensitivity and specificity for all possible thresholds
SAS/STAT Procedures: FREQ, LOGISTIC, MIXED and NLMIXED can be used to perform ROC curve analysis
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ROC (Receiver Operating Characteristic) Curve1
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Definition2
Sensitivity is the probability of a test will be positive given a patient with the disease Specificity is the probability of a test will be negative given a patient without the disease
Sensitivity = TP/(TP+FN) = a/(a+b) Specificity = TN/(TN+FP) = d/(c+d)
Positive predictive value (PPV) = TP/(TP+FP) = a/(a+c) Negative predictive value (NPV) = TN/(TN+FN) = d/(b+d)
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