Which statement best describes the positive predictive value (PPV) of a diagnostic test?

Which statement best describes the positive predictive value (PPV) of a diagnostic test?

• A. The proportion of patients who test positive who actually have the disease.
• B. The proportion of patients with disease who test positive.
• C. The probability that a negative test excludes disease.
• D. The probability that the disease is present in the general population.

Answer: (A)

PPV is the probability that someone who tests positive actually has the disease. In other words, it’s the fraction of true positives among all positive test results. It can be expressed as true positives divided by (true positives plus false positives). This distinguishes it from sensitivity (the proportion of diseased individuals who test positive) and from negative predictive value (the probability that a negative test truly means the person does not have the disease). PPV also depends on how common the disease is in the tested population—higher prevalence increases PPV, while lower prevalence decreases it. So the statement “the proportion of patients who test positive who actually have the disease” is the best description of PPV.

PPV is the probability that someone who tests positive actually has the disease. In other words, it’s the fraction of true positives among all positive test results. It can be expressed as true positives divided by (true positives plus false positives). This distinguishes it from sensitivity (the proportion of diseased individuals who test positive) and from negative predictive value (the probability that a negative test truly means the person does not have the disease). PPV also depends on how common the disease is in the tested population—higher prevalence increases PPV, while lower prevalence decreases it. So the statement “the proportion of patients who test positive who actually have the disease” is the best description of PPV.