Which of the following is true about positive and negative predictive values?

Positive and negative predictive values are metrics that indicate how well a test performs in a specific population with respect to the presence of a disease. The correct answer states that these values are directly affected by the prevalence of the disease within the population being tested.

When the prevalence of a disease is high, a positive predictive value (PPV) increases, meaning that a positive test result is more likely to be a true positive. Conversely, a low prevalence generally leads to a lower PPV, as a positive result is more likely due to false positives. Similarly, negative predictive value (NPV) is influenced by prevalence; as prevalence decreases, NPV tends to increase, indicating that a negative result is more likely to be a true negative.

Understanding this relationship is crucial for interpreting test results effectively, especially in different populations where disease prevalence may vary significantly. Thus, acknowledging the impact of disease prevalence on both positive and negative predictive values is key to understanding their clinical significance.