Different aims are possible. More general one is to find out a cut-off value for a (continuous) biomarker to diagnose a disease. Namely after finding that cut-off you say that those who have greater (or less) values than that cut-off is diseased and those who have less (or greater) are healthy.
Hi,
I'll try to explain ROC curve analysis using a simple example.
Imagine a study evaluating a new test that screens people for a disease. Each person taking the test either has or does not have the disease. The test outcome can be positive (classifying the person as having the disease) or negative (classifying the person as not having the disease).
For now, suppose the outcome of a medical test results in a continuous-scale measurement. Let t be a threshold (sometimes called a cutoff) value of the diagnostic test used to classify subjects. Assume that subjects with diagnostic test values less than or equal to t are classified as non-diseased and that subjects with diagnostic test values greater than t are classified as diseased, and let m and n denote the number of subjects in each group.
The test results for each subject may or may not match the subject's actual status. In that setting:
True positive: Sick people correctly identified as sick
False positive: Healthy people incorrectly identified as sick
True negative: Healthy people correctly identified as healthy
False negative: Sick people incorrectly identified as healthy
In general, Positive = identified and negative = rejected. Therefore:
True positive = correctly identified
False positive = incorrectly identified
True negative = correctly rejected
False negative = incorrectly rejected
To estimate classification accuracy using standard ROC methods, the disease status for each patient is measured without error. The true disease status often is referred to as the gold standard. The gold standard may be available from clinical follow-up, surgical verification, and autopsy; in some cases, it is adjudicated by a committee of experts.
The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. Discrimination threshold: largest change in a value of a quantity being measured that causes no detectable change in the corresponding indication. An ROC curve is a plot of sensitivity on the y axis against (1−specificity) on the x axis for varying values of the threshold t.
When evaluating a continuous-scale diagnostic test, we need to account for the changes of specificity and sensitivity when the test threshold t varies.
The 45° diagonal line connecting (0,0) to (1,1) is the ROC curve corresponding to random chance. The ROC curve for the gold standard is the line connecting (0,0) to (0,1) and (0,1) to (1,1). Generally, ROC curves lie between these 2 extremes. The area under the ROC curve is a summary measure that essentially averages diagnostic accuracy across the spectrum of test values.
See also http://circ.ahajournals.org/content/115/5/654
Hope this helps.
Dear @Mehmet Sinan Iyisoy thank you very much for your feedback.
Dear @Alexander Egoyan, Greerings! Thank you for your details feedback which is very helpful for me. Can you kindly tell me how we can use this test is public health research? Looking forward to hearing you soon. With the best regards. Palash
Dear Palash,
I appreciate your interest in our work and thank you for your comments.
ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.
The diagnostic performance of a test is the accuracy of a test to discriminate diseased cases from normal controls.
ROC curves can also be used to compare the diagnostic performance of two or more laboratory tests.
ROC Curves plot the true positive rate (sensitivity) against the false positive rate (1-specificity) for the different possible cutpoints of a diagnostic test. Each point on the ROC curve represents a sensitivity/specificity pair.
The closer the curve follows the left side border and the top border, the more accurate the test.
The closer the curve is to the 45-degree diagonal, the less accurate the test.
See also https://www.mailman.columbia.edu/research/population-health-methods/evaluating-risk-prediction-roc-curves
and http://www.scielo.br/scielo.php?pid=S0021-75572009000100008&script=sci_arttext&tlng=en
Hope this helps.
Dear @Alexander Egoyan, Greetings & thank you very much for your kind feedback.