Abstract
Diagnostic tests are important means in clinical practice. To assess the performance of a diagnostic test, we commonly need to compare its results to those obtained from a gold standard test. The test sensitivity is the probability of having a positive test in a diseased-patient; the specificity, a negative test result in a disease-free person. However, none of these indices are useful for clinicians who are looking for the inverse probabilities, i.e., the probabilities of the presence and absence of the disease in a person with a positive and negative test result, respectively, the so-called positive and negative predictive values. Likelihood ratios are other performance indices, which are not readily comprehensible to clinicians. There is another index proposed that looks more comprehensible to practicing physicians - the number needed to misdiagnose. It is the number of people who need to be tested in order to find one misdiagnosed (a false positive or a false negative result). For tests with continuous results, it is necessary to set a cut-off point, the choice of which affects the test performance. To arrive at a correct estimation of test performance indices, it is important to use a properly designed study and to consider various aspects that could potentially compromise the validity of the study, including the choice of the gold standard and the population study, among other things. Finally, it may be possible to derive the performance indices of a test solely based on the shape of the distribution of its results in a given group of people.