Which formula defines Positive Predictive Value?

Which formula defines Positive Predictive Value?

• A. PPV = TP / (TP + FP)
• B. NPV = TN / (FN + TN)
• C. Sensitivity = TP / (TP + FN)
• D. Specificity = TN / (TN + FP)

Answer: (A)

Positive predictive value is the probability that a person truly has the disease given that their test result is positive. The formula reflects that by taking the number of true positives and dividing by all positive test results (true positives plus false positives). In symbols, PPV = TP / (TP + FP). This is exactly why that option is the correct one. Understanding the other metrics helps place PPV in context: sensitivity looks at how well the test identifies those with disease (TP / (TP + FN)); specificity looks at how well it identifies those without disease (TN / (TN + FP)); and negative predictive value looks at the chance a negative test means no disease (TN / (FN + TN)). PPV depends on how many positives are true versus false, which also shifts with disease prevalence in the tested population.

Positive predictive value is the probability that a person truly has the disease given that their test result is positive. The formula reflects that by taking the number of true positives and dividing by all positive test results (true positives plus false positives). In symbols, PPV = TP / (TP + FP). This is exactly why that option is the correct one.

Understanding the other metrics helps place PPV in context: sensitivity looks at how well the test identifies those with disease (TP / (TP + FN)); specificity looks at how well it identifies those without disease (TN / (TN + FP)); and negative predictive value looks at the chance a negative test means no disease (TN / (FN + TN)). PPV depends on how many positives are true versus false, which also shifts with disease prevalence in the tested population.