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Background and Design.- Diagnostic tests are widely used in many areas of our modern technological society, but they are of particular importance in medicine, where early and accurate diagnosis can decrease the morbidity and mortality of disease.
As new diagnostic tests are introduced, their impact on patients health must be determined. It is often important to compare various diagnostic tests with each other for specific clinical conditions in order to determine which one is the best to use.
A perfect diagnostic test would be one with no false positive or false negative results and would be represented by a line that started at the origin and went up the y-axis to a sensitivity of 1, and then across to false positive rate of 0.
The methods of ROC curve, Kappa coefficient, Youden index, and Mc-Nemar test are used to present and/or compare the performances of one or more diagnostic tests.
ROC curves plot sensitivity against 1- specificity for the different cut-off points. Most popular information is the area under the curve, which has been called the best index of detectability.
Results and Conclusion.- In this study, we expressed mathematical basics and calculations of ROC curve analysis and the other methods within hypothetical examples.