What inputs do I need to use this calculator?
You need four values from your diagnostic study: True Positives (TP), False Negatives (FN), False Positives (FP), and True Negatives (TN). Optionally, you can enter a known disease prevalence percentage if your study sample does not reflect the real-world prevalence of the condition. See also our calculate AUC (Area Under Curve) AUC Score.
How is sensitivity calculated?
Sensitivity = TP / (TP + FN). It measures the proportion of people who truly have the disease that the test correctly identifies as positive. A highly sensitive test rarely misses true cases, making it useful for ruling out disease when the result is negative.
How is specificity calculated?
Specificity = TN / (TN + FP). It measures the proportion of people who do not have the disease that the test correctly identifies as negative. A highly specific test rarely flags healthy people as sick, making it useful for ruling in disease when the result is positive.
What is the difference between PPV and sensitivity?
Sensitivity tells you how well the test detects true disease in people who actually have it — it is independent of prevalence. PPV (Positive Predictive Value) tells you the probability that a person who tests positive actually has the disease, and it depends heavily on how common the disease is in the tested population. You might also find our calculate Likelihood Ratio useful.
How do I calculate the positive and negative predictive values?
PPV = (Sensitivity × Prevalence) / [(Sensitivity × Prevalence) + ((1 − Specificity) × (1 − Prevalence))]. NPV = (Specificity × (1 − Prevalence)) / [((1 − Sensitivity) × Prevalence) + (Specificity × (1 − Prevalence))]. Both values shift significantly as disease prevalence changes, which is why entering the correct prevalence matters.
What is the likelihood ratio and how do I interpret it?
The Positive Likelihood Ratio (LR+) = Sensitivity / (1 − Specificity). It tells you how much a positive test result increases the odds of disease. LR+ > 10 is considered strong evidence for disease. The Negative Likelihood Ratio (LR−) = (1 − Sensitivity) / Specificity. LR− < 0.1 is considered strong evidence against disease.
How is overall test accuracy calculated?
Accuracy = (TP + TN) / (TP + TN + FP + FN). It represents the proportion of all test results — both positive and negative — that are correct. However, accuracy can be misleading when disease prevalence is very low or very high, so always consider sensitivity, specificity, PPV, and NPV together.
Why does disease prevalence affect PPV and NPV but not sensitivity and specificity?
Sensitivity and specificity are intrinsic properties of the test itself, calculated only from diseased and non-diseased groups respectively. PPV and NPV depend on how many people in the population actually have the disease. In a low-prevalence population, even a highly specific test will produce many false positives relative to true positives, driving PPV down. Check out our Sensitivity Calculator as well.