Table of Contents
What Is Post-Test Probability?
Post-test probability is the probability that a patient has a condition after a diagnostic test result is known. It updates the pre-test probability (prior probability or prevalence) using the test's accuracy characteristics: sensitivity and specificity. This is a direct application of Bayes' theorem in clinical medicine.
The concept is critical in evidence-based medicine. A positive test result for a rare disease may still have a low post-test probability if the disease prevalence is very low, even with a highly sensitive test. Understanding post-test probability helps clinicians make better diagnostic and treatment decisions.
Bayes' Theorem and Likelihood Ratios
Likelihood Ratio Interpretation
| Positive LR | Negative LR | Clinical Impact |
|---|---|---|
| > 10 | < 0.1 | Large, often conclusive |
| 5 - 10 | 0.1 - 0.2 | Moderate shift in probability |
| 2 - 5 | 0.2 - 0.5 | Small but sometimes important |
| 1 - 2 | 0.5 - 1.0 | Minimal, rarely important |
Frequently Asked Questions
What is the difference between sensitivity and specificity?
Sensitivity is the probability that a test correctly identifies patients who have the condition (true positive rate). Specificity is the probability that a test correctly identifies patients who do not have the condition (true negative rate). A test with 90% sensitivity will miss 10% of true cases.
Why does prevalence matter so much?
Even a very accurate test can produce misleading results when the condition is rare. If a disease affects 1 in 1000 people and you test everyone, even a 99% specific test will produce more false positives than true positives. This is why screening tests are typically reserved for populations with higher pre-test probability.