Which formula defines Negative Predictive Value?

Which formula defines Negative Predictive Value?

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

Answer: (A)

Negative predictive value indicates how confident we can be that a negative test result truly means the person does not have the disease. It answers: among those who test negative, what proportion are actually disease-free? The correct formula is true negatives divided by the sum of true negatives and false negatives: NPV = TN / (FN + TN). True negatives are people without disease who test negative, while false negatives are those with disease who test negative. So the negative-tested group contains true negatives (correct negatives) and false negatives (missed cases), and NPV is the fraction that are truly disease-free. For comparison, positive predictive value uses true positives over all positive results (TP/(TP+FP)); sensitivity uses true positives over all with disease (TP/(TP+FN)); and specificity uses true negatives over all without disease (TN/(TN+FP)).

Negative predictive value indicates how confident we can be that a negative test result truly means the person does not have the disease. It answers: among those who test negative, what proportion are actually disease-free? The correct formula is true negatives divided by the sum of true negatives and false negatives: NPV = TN / (FN + TN). True negatives are people without disease who test negative, while false negatives are those with disease who test negative. So the negative-tested group contains true negatives (correct negatives) and false negatives (missed cases), and NPV is the fraction that are truly disease-free.

For comparison, positive predictive value uses true positives over all positive results (TP/(TP+FP)); sensitivity uses true positives over all with disease (TP/(TP+FN)); and specificity uses true negatives over all without disease (TN/(TN+FP)).