Which formula represents sensitivity?

Which formula represents sensitivity?

• A. TN / (TN + FP)
• B. TP / (TP + FN)
• C. TP / (FP + TP)
• D. TN / (FN + TN)

Answer: (B)

Sensitivity measures how well a test detects people who actually have the condition. It is calculated as true positives divided by all those who truly have the condition: TP / (TP + FN). This means it reflects the proportion of actual positives that the test correctly identifies; a high sensitivity indicates few false negatives. For context, if 100 people have the disease and the test picks up 90 of them, the sensitivity is 90%. The other metrics describe different ideas: specificity is TN / (TN + FP), the proportion of actual negatives correctly identified; negative predictive value is TN / (FN + TN), the probability that a person testing negative truly does not have the disease; and positive predictive value is TP / (TP + FP), the probability that a person testing positive truly has the disease.

Sensitivity measures how well a test detects people who actually have the condition. It is calculated as true positives divided by all those who truly have the condition: TP / (TP + FN). This means it reflects the proportion of actual positives that the test correctly identifies; a high sensitivity indicates few false negatives. For context, if 100 people have the disease and the test picks up 90 of them, the sensitivity is 90%. The other metrics describe different ideas: specificity is TN / (TN + FP), the proportion of actual negatives correctly identified; negative predictive value is TN / (FN + TN), the probability that a person testing negative truly does not have the disease; and positive predictive value is TP / (TP + FP), the probability that a person testing positive truly has the disease.