The y-axis showing sensitivity (= true positive fraction = TP/(TP+FN)).The x-axis showing 1 – specificity (= false positive fraction = FP/(FP+TN)).When the cut-off is increased to 500 µg/L, the sensitivity decreases to 92 % and the specificity increases to 79 %.Īn ROC curve shows the relationship between clinical sensitivity and specificity for every possible cut-off. When 400 µg/L is chosen as the analyte concentration cut-off, the sensitivity is 100 % and the specificity is 54 %.

II demonstrate the trade-off between sensitivity and specificity. Thus you will get an increase in sensitivity or specificity at the expense of lowering the other parameter when you change the cut-off. When you change the cut-off, you will get other values for true positives and negatives and false positives and negatives, but the number of all with disease is the same and so is the number of all without disease. The cut-off determines the clinical sensitivity (fraction of true positives to all with disease) and specificity (fraction of true negatives to all without disease). TABLE I : Comparing a method with the clinical truthīefore you make a table like TABLE I you have to decide your cut-off for distinguishing healthy from sick. These concepts are used when you compare the results of a test with the clinical truth, which is established by the use of diagnostic procedures not involving the test in question. To make an ROC curve you have to be familiar with the concepts of true positive, true negative, false positive and false negative.

In addition, the area under the ROC curve gives an idea about the benefit of using the test(s) in question. Now ROC curves are frequently used to show the connection between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. The initial research was motivated by the desire to determine how the US RADAR 'receiver operators' had missed the Japanese aircraft. ROC curves were first employed in the study of discriminator systems for the detection of radio signals in the presence of noise in the 1940s, following the attack on Pearl Harbor. The term ROC stands for Receiver Operating Characteristic.

Excel graph axis label overlap