Which statement best describes how predictive values relate to prevalence and test characteristics?

• A. Predictive values are unaffected by prevalence.
• B. Predictive values depend only on sensitivity.
• C. Predictive values are influenced by disease prevalence and specificity, less by sensitivity.
• D. Predictive values depend only on the number of tests performed.

Answer: (C)

Predictive values are not fixed properties of a test; they depend on how common the disease is in the population and on how accurately the test distinguishes cases from non-cases. The positive predictive value—the chance that a person with a positive result truly has the disease—goes up as disease prevalence rises and as the test reduces false positives (which is tied to specificity). The negative predictive value—the chance that a person with a negative result truly does not have the disease—also shifts with prevalence but is more closely related to sensitivity, which governs true positives and false negatives. In general, predictive values vary with prevalence and the test’s performance, and they are particularly sensitive to specificity when considering the proportion of positives that are true positives. Among the statements, describing predictive values as influenced by disease prevalence and specificity captures this dependence best. They aren’t determined by the number of tests alone, nor by sensitivity alone, and they do change with prevalence.