Sensitivity and Specificity
Statistics play an important part in medical decision making
Whether a physician uses a blood test or asks patients about symptoms to test if a patient could have a disease, the physician has to fully understand the concepts of sensitivity, specificity, and their cousins positive predictive and negative predictive values. A basic understanding of these concepts is extremely important in order to properly diagnose a condition objectively.
Sensitivity is a measure of false negatives. If a medical test has a high sensitivity it is would be a very good test to rule out a disease and would be ideally suited for screening purposes. Specificity, on the other hand, is a measure of false positives. Thus, a medical test with high specificity is very good at ruling in a condition and would ideally be suited for patients who already have a high probability of a disease based on a screening test.
Positive predictive value refers to the chance that a positive test result will be correct. That is, it looks at all the positive test results. On the other hand, negative predictive value is concerned only with negative test results ( defined as the proportion of subjects with a negative test result who are correctly diagnosed). The interesting thing about positive and negative predictive values is that they change if the prevalence of the disease changes. If the prevalence of the condition is very low, even if the specificity ratio is very high, then most positives will be false positives.
Examples of statistics in action with thyroid disease: fine needle aspiration