Enter the four cells of your 2×2 table and get sensitivity, specificity, predictive values, likelihood ratios and accuracy, each with a 95% confidence interval. Your counts never leave your browser.
Sensitivity and specificity travel with the test. Predictive values do not, they shift with how common the disease is. Reporting the right pair, with intervals, is what a reviewer expects.
Diagnostic accuracy studies follow STARD, which expects sensitivity and specificity with 95% confidence intervals. This gives you both.
Enter a prevalence for your setting and the tool re-expresses the predictive values for it, the classic reason a strong test disappoints in practice.
Likelihood ratios fold sensitivity and specificity into a single number you can apply to one patient, and they come with intervals here.
ROC curves, optimal cut-offs, the comparison of two tests and paired or clustered designs need more than a 2×2. Rigora’s statistics service delivers the analysis, the figures and the reporting, done by a publishing physician-scientist.
Sensitivity is the proportion of people with the disease who test positive. Specificity is the proportion without the disease who test negative. Both are properties of the test and do not depend on how common the disease is.
Predictive values answer the patient’s question, given my result do I have the disease, and that depends on how common the disease is. The same test has a lower positive predictive value in a low-prevalence screening setting than in a high-prevalence clinic.
A likelihood ratio combines sensitivity and specificity into one number that tells you how much a result changes the odds of disease. A positive likelihood ratio above 10, or a negative one below 0.1, is often called strong evidence.
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