1. What is 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 are fundamental metrics for evaluating diagnostic tests.

2. How Does the Calculator Work?

The calculator uses the following equations:

Where:

• \( TP \) — True Positives (correctly identified positives)
• \( FN \) — False Negatives (positives incorrectly identified as negative)
• \( TN \) — True Negatives (correctly identified negatives)
• \( FP \) — False Positives (negatives incorrectly identified as positive)

3. Importance of Sensitivity and Specificity

Details: Sensitivity is crucial when missing a positive case has severe consequences. Specificity is important when falsely labeling someone as positive has serious implications. Together they describe a test's discriminative ability.

4. Using the Calculator

Tips: Enter the counts from your 2×2 contingency table. All values must be non-negative integers. Results are displayed as proportions between 0 and 1.

5. Frequently Asked Questions (FAQ)

Q1: What is a good sensitivity value?
A: Generally ≥0.9 is excellent, 0.8-0.9 is good, though this depends on the clinical context and consequences of false negatives.

Q2: What is a good specificity value?
A: Similar to sensitivity, ≥0.9 is excellent, 0.8-0.9 is good, with context determining acceptable thresholds.

Q3: Can a test have both 100% sensitivity and specificity?
A: In theory yes, but in practice there's typically a trade-off between the two metrics for real-world tests.

Q4: What's the relationship with PPV and NPV?
A: Positive/Negative Predictive Values also depend on disease prevalence, while sensitivity/specificity are test characteristics independent of prevalence.

Q5: How do I create a 2×2 table?
A: Compare test results against a gold standard, counting TP, FP, TN, FN cases based on agreement/disagreement.