Diagnostic testing in psychiatry: insights and examples from a Bayesian perspective

Abstract

Objective To demonstrate the application of Bayes’ theorem to diagnostic testing in clinical settings, especially with respect to rare diseases, enhancing an understanding of pre-test probability and its implications. Conclusion Bayes’ theorem enables the revision of the conditional probabilities of an event occurring when new information is acquired. It demonstrates that when the prevalence of a disease is very low, there are a high number of false positives, thereby reducing the clinical utility and cost benefit profile of the diagnostic test, even in the presence of relatively high sensitivities and specificities of the chosen test.