Use this diagnostic test calculator to estimate how a medical or screening test performs in a real population. Enter sensitivity, specificity, and prevalence to calculate predictive values, likelihood ratios, and an expected confusion matrix.
What this diagnostic test calculator tells you
Many people focus only on sensitivity and specificity, but those do not directly answer the questions clinicians and patients ask: “If my test is positive, what is the chance I actually have the disease?” and “If my test is negative, what is the chance I am disease-free?” This tool bridges that gap by calculating:
- PPV (Positive Predictive Value) — chance disease is present after a positive result
- NPV (Negative Predictive Value) — chance disease is absent after a negative result
- Likelihood Ratios (LR+ and LR-) — strength of evidence from positive or negative test results
- Expected counts — estimated true positives, false positives, true negatives, and false negatives in your chosen population
Why prevalence changes everything
Prevalence is often the hidden driver of interpretation. Even a highly accurate test can produce many false positives when prevalence is low. Conversely, in high-risk populations, positive results are more likely to be true positives.
This is why diagnostic testing should be interpreted in context: symptoms, risk factors, pre-test probability, and test quality all matter.
Quick definitions
Sensitivity
Of all people who truly have the condition, what fraction does the test correctly identify as positive?
Specificity
Of all people who truly do not have the condition, what fraction does the test correctly identify as negative?
PPV and NPV
These are clinically intuitive because they translate test performance into patient-centered probabilities after testing.
How to use this tool in practice
- Use prevalence that matches your actual setting (general population vs specialist clinic).
- Check false positives and false negatives, not just one summary metric.
- Compare scenarios by adjusting prevalence or changing test characteristics.
- Use likelihood ratios to update pre-test odds if you perform formal Bayesian reasoning.
Interpretation tips
A strong screening strategy usually prioritizes sensitivity to avoid missed disease, while confirmatory testing often prioritizes specificity to avoid false alarms. There is no universal “best” balance; it depends on disease severity, treatment risk, and consequences of diagnostic error.
Important note
This calculator is for educational and planning purposes. It does not provide medical advice, diagnosis, or treatment recommendations. Real decisions should be made with qualified healthcare professionals using complete clinical information.