Do positive predictive value (PPV) and negative predictive value (NPV) change with prevalence?

Positive predictive value (PPV) and negative predictive value (NPV) are both influenced by the prevalence of the condition being tested.

PPV is the probability that subjects with a positive test truly have the disease. As the prevalence of a disease increases, the PPV also generally increases because there are more true positives relative to false positives. Conversely, when prevalence is low, there may be more false positives, thus decreasing the PPV.

NPV is the probability that subjects with a negative test truly do not have the disease. Similar to PPV, NPV is affected by prevalence. As prevalence decreases, the NPV tends to increase since there are fewer people with the disease, thereby decreasing the likelihood of false negatives relative to true negatives.

Both PPV and NPV are dependent on the relationship between true positive and false positive rates (related to PPV) and true negative and false negative rates (related to NPV) in the context of prevalence. Therefore, with changes in the prevalence of a condition, both the PPV and NPV will also change accordingly. Understanding this relationship is crucial in interpreting diagnostic tests, as it highlights the dynamic nature of test performance based on the population being evaluated.