Define positive and negative predictive values?

• A. The probability that the disease is present in the population
• B. The probability that an animal does not have the disease given a negative test result
• C. The probability that the test is positive regardless of disease status
• D. The probability that an animal has the disease given a positive test result

Answer: (D)

Predictive values address what a test result tells you about the actual disease status in an individual. The positive predictive value is the probability that the subject truly has the disease given that the test result is positive. In notation, that’s P(Disease | Test positive). The negative predictive value is the probability that the subject truly does not have the disease given a negative test result, P(No disease | Test negative). The best way to state the positive predictive value is: the probability that an animal has the disease given a positive test result. The other ideas describe different concepts—one refers to how common the disease is in the population (prevalence), and another refers to the overall chance of a positive test regardless of disease status—so they don’t define predictive value. The negative predictive value is the complementary concept, describing the chance of being disease-free given a negative result, which isn’t captured by the main statement here.