# Positive Predictive Value Calculator

Determines the proportion of positive results in a diagnostic test, that are true positive.

The positive predictive value is defined as the number of true positives divided by the number of positive calls. The ideal value of the PPV, with a perfect test, is 100% whilst the worst possible value is 0.

• Positive Predictive Value = TP / (TP + FP)
• Positive Predictive Value = Sensitivity x Prevalence / (Sensitivity x Prevalence + (1 - Specificity) x (1 - Prevalence))

True Positive (TP)
False Positive (FP)
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Steps on how to print your input & results:

2. Then you can click on the Print button to open a PDF in a separate window with the inputs and results. You can further save the PDF or print it.

Please note that once you have closed the PDF you need to click on the Calculate button before you try opening it again, otherwise the input and/or results may not appear in the pdf.

## Positive Predictive Value Explained

The positive predictive value is defined as the number of true positives divided by the number of positive calls. Along with negative predictive value, PPV measures the performance of a diagnostic test and an ideal value, with a perfect test, is 100% whilst the worst possible value is 0.

• Positive Predictive Value = TP / (TP + FP)

Where:

True positive (TP) –is the outcome where the model correctly predicts positive class (condition is correctly detected when present);

False positive (FP) – which is the outcome where the model incorrectly predicts positive class (condition is detected despite being absent);

• Positive Predictive Value = Sensitivity x Prevalence / (Sensitivity x Prevalence + (1 - Specificity) x (1-Prevalence))

It is also known as Precision, and is used to indicate how probable it is that, in case of a positive test, the patient has the specified disease.

## References

Altman DG, Machin D, Bryant TN, Gardner MJ (Eds) (2000) Statistics with confidence, 2nd ed. BMJ Books.

Mercaldo ND, Lau KF, Zhou XH. Confidence intervals for predictive values with an emphasis to case-control studies. Stat Med. 2007; 26(10):2170-2183.

Powers, David M W. Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation (PDF). Journal of Machine Learning Technologies. 2011; 2 (1): 37–63.

Specialty: Research

Abbreviation: PPV

Article By: Denise Nedea

Published On: July 10, 2020

Last Checked: July 10, 2020

Next Review: July 10, 2025