ROC curve analysis: predictive values

Command: Statistics
Next selectROC curves
Next selectPredictive values

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

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).

    Positive predictive value formula according to Bayes' theorem.

  • Negative predictive value (NPV): probability that the disease is not present when the test is negative (expressed as a percentage).

    Negative  predictive value formula according to Bayes' theorem.

  • When the sample sizes in the diseased and normal groups are known, MedCalc calculates confidence intervals for the predictive values using the standard logit method given by Mercaldo et al. 2007.

Literature

  • Mercaldo ND, Lau KF, Zhou XH (2007) Confidence intervals for predictive values with an emphasis to case-control studies. Statistics in Medicine 26:2170-2183.

See also

This site uses cookies to store information on your computer. More info...