1 Sensitivity , Specificity , Accuracy , Associated Confidence Interval and ROC Analysis with Practical SAS

Diagnosis tests include different kinds of information, such as medical tests (e.g. blood tests, X-rays, MRA), medical signs (clubbing of the fingers, a sign of lung disease), or symptoms (e.g. pain in a particular pattern). Doctor’s decisions of medical treatment rely on diagnosis tests, which makes the accuracy of a diagnosis is essential in medical care. Fortunately, the attributes of the diagnosis tests can be measured. For a given disease condition, the best possible test can be chosen based on these attributes. Sensitivity, specificity and accuracy are widely used statistics to describe a diagnostic test. In particular, they are used to quantify how good and reliable a test is. Sensitivity evaluates how good the test is at detecting a positive disease. Specificity estimates how likely patients without disease can be correctly ruled out. ROC curve is a graphic presentation of the relationship between both sensitivity and specificity and it helps to decide the optimal model through determining the best threshold for the diagnostic test. Accuracy measures how correct a diagnostic test identifies and excludes a given condition. Accuracy of a diagnostic test can be determined from sensitivity and specificity with the presence of prevalence. Given the importance of these statistics in disease diagnosis and the terms are easily confused, it is important to get familiar with how they work, it helps us better understand when to use, how to implement them, and how to interpret the results. The importance and popularity of these statistics urges for a thorough review along with practical SAS examples.