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What is PPV and NPV?
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The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure.
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For any given test (i.e. sensitivity and specificity remain the same) as prevalence decreases, the PPV decreases because there will be more false positives for every true positive.
Negative predictive value (NPV)
Prevalence PPV NPV 1% 8% >99% 10% 50% 99% 20% 69% 97% 50% 90% 90%
Additionally, is PPV the same as sensitivity? Sensitivity is the “true positive rate,” equivalent to a/a c. Specificity is the “true negative rate,” equivalent to d/b d. PPV is the proportion of people with a positive test result who actually have the disease (a/a b); NPV is the proportion of those with a negative result who do not have the disease (d/c d).Similarly one may ask, how do you calculate PPV?
To calculate the positive predictive value (PPV), divide TP by (TP FP). In the case above, that would be 95/(95 90)= 51.4%. The positive predictive value tells us how likely someone is to have the characteristic if the test is positive.
Why does prevalence affect PPV and NPV?
Prevalence thus impacts the positive predictive value (PPV) and negative predictive value (NPV) of tests. As the prevalence increases, the PPV also increases but the NPV decreases. Similarly, as the prevalence decreases the PPV decreases while the NPV increases.
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