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Post-Test Probability Calculator — Bayesian Diagnostic Test Analysis

Calculate post-test probability using Bayes' theorem. Enter confusion matrix or sensitivity/specificity to find positive and negative likelihood ratios, pre-test and post-test probabilities.

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Fill in the form on the left to see the post-test probability results.

Post-Test Probability Calculator

The post-test probability calculator is a Bayesian tool that helps clinicians and researchers determine the probability of a disease or condition after a diagnostic test has been performed. It combines pre-test probability (prevalence) with the test's sensitivity and specificity through likelihood ratios.

How to Use

Choose between two input modes:

  • Confusion Matrix — enter the number of True Positives (TP), False Negatives (FN), False Positives (FP), and True Negatives (TN).
  • Direct Input — enter sensitivity, specificity, and pre-test probability directly as percentages or decimals.

Key Formulas

Sensitivity = TP / (TP + FN)
Measures how well the test detects a true positive (diseased) patient.

Specificity = TN / (FP + TN)
Measures how well the test identifies true negatives (healthy patients).

Positive Likelihood Ratio (LR+) = Sensitivity / (1 − Specificity)
How much more likely a positive result is in a diseased vs. healthy person.

Negative Likelihood Ratio (LR−) = (1 − Sensitivity) / Specificity
How much less likely a negative result is in a diseased vs. healthy person.

Pre-test Odds = Prevalence / (1 − Prevalence)

Post-test Odds (positive test) = Pre-test Odds × LR+
Post-test Probability (positive test) = Post-test Odds / (1 + Post-test Odds)

Post-test Odds (negative test) = Pre-test Odds × LR−
Post-test Probability (negative test) = Post-test Odds / (1 + Post-test Odds)

Interpreting Likelihood Ratios

  • LR+ > 10 — strongly confirms the presence of the disease
  • LR+ 2–10 — moderate evidence for the disease
  • LR− < 0.1 — strongly rules out the disease
  • LR− 0.1–0.5 — moderate evidence against the disease

Pre-test vs Post-test Probability

The pre-test probability (also called prevalence) is the probability of disease before the test is performed, based on population data or clinical judgment. The post-test probability updates this estimate after receiving the test result — either positive or negative.

Example

Suppose a disease has a 10% prevalence and a test has 80% sensitivity and 89% specificity:

  • LR+ = 0.80 / (1 − 0.89) = 7.27
  • Pre-test odds = 0.10 / 0.90 = 0.111
  • Post-test odds (positive) = 0.111 × 7.27 = 0.808
  • Post-test probability (positive) = 0.808 / 1.808 ≈ 44.7%

Calculation History

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