Sensitivity and Specificity Calculator

Sensitivity, Specificity, and Predictive Values

This calculator evaluates diagnostic test performance from a 2×2 confusion matrix. It reports sensitivity, specificity, accuracy, likelihood ratios, and prevalence-adjusted PPV and NPV. Related tools: Sensitivity Calculator, Post-Test Probability Calculator, and Relative Risk Calculator.

Core Formulas

$$Sensitivity = \frac{TP}{TP + FN}$$

$$Specificity = \frac{TN}{TN + FP}$$

$$Accuracy = \frac{TP + TN}{TP + TN + FP + FN}$$

$$LR_+ = \frac{Sensitivity}{1 - Specificity}$$

$$LR_- = \frac{1 - Sensitivity}{Specificity}$$

PPV and NPV With Prevalence

Predictive values depend on disease prevalence. The calculator uses prevalence to estimate adjusted positive and negative predictive values, which describe how trustworthy a positive or negative test result is in the target population.

Example

With TP = 48, FN = 12, FP = 18, TN = 122, sensitivity is 80%, specificity is about 87.14%, and accuracy is 85%.

Frequently Asked Questions

What is a confusion matrix?

It is a table of test outcomes: true positives, false positives, false negatives, and true negatives.

Why does prevalence affect PPV and NPV?

When a disease is rare, even a good test can produce many false positives among all positive results. Prevalence shifts the balance of true and false outcomes.

What is a likelihood ratio?

It compares the odds of a test result in diseased people with the odds in healthy people. LR+ applies to positive results; LR- applies to negative results.

Is accuracy enough on its own?

Not always. Accuracy can look high in imbalanced datasets even when sensitivity or specificity is poor.

Can I use this for screening and confirmatory tests?

Yes, as long as you enter the correct TP, FP, FN, and TN counts from the study or validation dataset.

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