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ROC curve analysis: predictive values

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Description

When you do have access to the raw data to perform ROC curve analysis, you can still calculate positive and negative predictive values for a test when the sensitivity and specificity of the test as well as the disease prevalence (or the pretest probability of disease) are known, using Bayes' theorem.

Required input

Enter the sensitivity and specificity of a test (expressed as percentages), and the disease prevalence (also expressed as a percentage).

Optionally you can enter the number of cases in the diseased and normal groups. These are the number of cases included in the study in which sensitivity and specificity were established. Input of these numbers will enable MedCalc to calculate 95% confidence intervals for the positive and negative predictive values.

ROC curve analysis: predictive values calculator

When these data are entered click the Test button, or press the Enter key to see the results.

Literature

See also

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