Negative Predictive Value Calculator

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


The negative predictive value is the ratio between the number of true negatives and number of negative calls.

It is one of the measures of the performance of a diagnostic test, with an ideal value being as close as possible to 100% and the worst possible value is 0.


  • Negative Predictive Value = TN / (TN + FN)
  • Negative Predictive Value = Specificity x (1 - Prevalence) / ((1 - Sensitivity) x Prevalence + Specificity x (1 - Prevalence))

True Negative (TN)
False Negative (FN)
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Steps on how to print your input & results:

1. Fill in the calculator/tool with your values and/or your answer choices and press Calculate.

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.


 

Negative Predictive Value Explained

The negative predictive value is the ratio between the number of true negatives and number of negative calls. Along with the positive predictive value, it is one of the measures of the performance of a diagnostic test, with an ideal value being as close as possible to 100% and the worst possible value is 0.

  • Negative Predictive Value = TN / (TN + FN)

Where:

True negative (TN) – is the outcome where the model correctly predicts negative class (condition is not detected when absent);

False negative (FN) – is the outcome where the model incorrectly predicts negative class (condition is not detected despite being present);

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

Negative predictive value is also known as the negative predictive agreement and refers to the probability that subjects with a negative test truly also do not have the specific disease in reality.

 

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

Article By: Denise Nedea

Published On: July 10, 2020

Last Checked: July 10, 2020

Next Review: July 10, 2025