ROC curve analysis: predictive values

Command

Statistics ROC curves Predictive values

Description

When you do not dispose of 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 total sample size in which sensitivity and specificity were established. If available, this allows calculating 95% confidence intervals for positive and negative predictive values.

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

• Positive predictive value (PPV): probability that the disease is present when the test is positive (expressed as a percentage).
• Negative predictive value (NPV): probability that the disease is not present when the test is negative (expressed as a percentage).
• If Sample size is known, the exact binomial confidence intervals for PPV and NPV are reported.

Literature

• Altman DG (1991) Practical statistics for medical research. London: Chapman and Hall.

See also

• ROC curves: Introduction
• Interactive dot diagram
• Plot versus criterion values
• Interval likelihood ratios
• Comparison of ROC curves