1. What Are Sensitivity and Specificity?

Sensitivity (true positive rate) measures the proportion of actual positives correctly identified. Specificity (true negative rate) measures the proportion of actual negatives correctly identified. These metrics are fundamental in evaluating diagnostic tests.

2. How Are They Calculated?

The formulas used in this calculator:

Where:

• True Positives (TP) — Correctly identified positive cases
• False Negatives (FN) — Positive cases incorrectly identified as negative
• True Negatives (TN) — Correctly identified negative cases
• False Positives (FP) — Negative cases incorrectly identified as positive

3. Clinical Importance

Details: High sensitivity tests are good for ruling out disease (SnNout), while high specificity tests are good for ruling in disease (SpPin). These metrics help evaluate diagnostic test performance.

4. Using the Calculator

Tips: Enter the counts from a 2×2 contingency table. All values must be non-negative integers. Results are presented as percentages.

5. Frequently Asked Questions (FAQ)

Q1: What's the difference between sensitivity and PPV?
A: Sensitivity measures how good the test is at detecting true cases, while positive predictive value (PPV) tells you the probability that a positive test result is truly positive.

Q2: Can a test have 100% sensitivity and specificity?
A: In theory yes, but in practice this is extremely rare as most tests involve trade-offs between sensitivity and specificity.

Q3: What is the relationship with ROC curves?
A: ROC curves plot sensitivity vs (1-specificity) across different test thresholds, showing the trade-off between these metrics.

Q4: How do prevalence affect these metrics?
A: Sensitivity and specificity are prevalence-independent, unlike predictive values which are affected by disease prevalence.

Q5: What are typical values for good tests?
A: Generally >90% for both is excellent, 80-90% is good, though optimal values depend on the clinical context.