The diagnostic test: terms and phenomena

The presentation “The diagnostic test: terms and phenomena” explains the key concepts and parameters of a diagnostic test, as well as phenomena and surprises that may occur. A scenario from the coronavirus pandemic is simulated to illustrate this. The article promotes understanding of diagnostic tests and serves as a good, concise introduction to this interesting and important topic. A diagnostic test is a diagnostic tool with a binary output: yes/no, positive/negative, sick/healthy, infected/not infected, suitable/not suitable, etc. Before the diagnostic test is used in diagnostics or screening, the two parameters sensitivity and specificity are determined. Sensitivity and specificity are random variables and characteristics of test quality. Prevalence (pre-test probability, base rate) indicates the frequency of sick or infected individuals in relation to the group being tested. After the test has been performed, the positive (negative) predictive value indicates the probability that a person with a positive (negative) test result is actually sick/infected (not sick/not infected). After the test has been performed, the positive (negative) predictive value indicates the probability that a person with a positive (negative) test result is actually ill/infected (not ill/not infected). Positive and negative predictive values are also random variables that depend on the three parameters of sensitivity, specificity, and prevalence. Various results of positive and negative predictive values are calculated and presented, varying sensitivity, specificity, and prevalence.