Negative Predictive Value (NPV) is a measure of a diagnostic test’s accuracy that represents the probability a person who tests negative truly does not have the condition. NPV focuses on how trustworthy a negative result is in real-world testing scenarios. Hence, it is best for interpreting an individual negative test result.
Conversely, if you’re evaluating a test’s inherent accuracy, it’s better to use sensitivity and specificity because a disease’s prevalence in the real world does not affected them.
Negative Predictive Value is one of several measures calculated from a confusion matrix, which categorizes test results into four mutually exclusive outcomes: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). NPV uses the counts of true negatives and false negatives to determine how often a negative test result truly indicates the absence of the condition.
NPV is calculated using this formula:
NPV = True Negatives / (True Negatives + False Negatives)
This calculation finds the proportion of all negative test results that are correct. Like positive predictive value (PPV), the NPV depends on the prevalence of the condition in the population being tested. NPV tends to be higher when the condition is rare.
For example, imagine a disease screening test applied to 1,000 people, where 900 are truly disease-free and test negative, and 30 people with the disease mistakenly test negative. The NPV would be:
NPV = 900 / (900 + 30) = 0.9677 or 96.8%
This result indicates there is a 96.8% chance that a person with a negative result truly does not have the disease.
Learn about the positive version of this statistic in my post: Positive Predictive Value: Mean, Formula, and Interpretation.
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