What term describes the probabilities that the disease status can be inferred from a test result?

• A. Sensitivity and specificity.
• B. Predictive values.
• C. Prevalence.
• D. Likelihood ratios.

Answer: (B)

Predictive values express the probability that disease status is correctly inferred from a test result. The positive predictive value is the chance that someone truly has the disease if the test is positive, while the negative predictive value is the chance that someone truly does not have the disease if the test is negative. These probabilities reflect both the test’s accuracy (sensitivity and specificity) and how common the disease is in the population (prevalence) through Bayes’ theorem. That direct link to the outcome for an individual makes predictive values the most relevant way to interpret what a given test result means for disease status. Sensitivity and specificity describe how the test performs in groups with or without disease, not the probability for a specific result in an individual. Prevalence is about how common the disease is overall and does not by itself give the probability that a test result reflects true disease status. Likelihood ratios quantify how a test result changes the odds of disease, but they are not probabilities of disease status themselves.