Which factors determine changes in positive and negative predictive values of a lab test?

• A. True prevalence of the population and test characteristics
• B. Sensitivity only
• C. Specificity only
• D. Sample size

Answer: (A)

Predictive values are conditional probabilities that depend on two things: how common the disease is in the population (prevalence) and how accurate the test is (sensitivity and specificity). If the disease is more prevalent, a positive test is more likely to reflect true disease, so the positive predictive value goes up, while the negative predictive value tends to go down. If the disease is rare, a negative result is more trustworthy, so the negative predictive value goes up and the positive predictive value tends to go down. The test’s sensitivity and specificity determine how often true cases and non-cases are correctly identified, shaping both predictive values regardless of prevalence. In short, the factors that change positive and negative predictive values are the true prevalence in the population and the test characteristics. Sample size affects how precisely we estimate these values but not the values themselves in a given population.