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

## TL;DR

This paper explains how Bayes' theorem helps understand diagnostic testing in psychiatry, especially for rare diseases, by showing how false positives can affect test usefulness.

## Contribution

The paper provides a Bayesian perspective on diagnostic testing, emphasizing the impact of low disease prevalence on test outcomes.

## Key findings

- Low disease prevalence leads to high false positives despite high test accuracy.
- Bayes' theorem helps revise probabilities based on new diagnostic information.
- Diagnostic utility decreases when pre-test probability is low.

## Abstract

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.

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.