Which performance metric is maximized when the false positive rate is minimized?

Which performance metric is maximized when the false positive rate is minimized?

• A. Specificity
• B. Sensitivity
• C. PPV
• D. NPV

Answer: (A)

Minimizing the false positive rate directly maximizes specificity. False positive rate is FP divided by (FP plus TN). If you reduce the number of false positives while true negatives stay the same, the false positive rate drops and, at the same time, the proportion of true negatives among all those without disease—specificity, which is TN divided by (FP plus TN)—increases. So lowering FP improves specificity, making it the metric that is maximized by minimizing false positives. Sensitivity depends on true positives and false negatives, not on FP in the same direct way. Positive predictive value and negative predictive value depend on disease prevalence and the balance of all test outcomes, so they’re not maximized simply by reducing false positives.

Minimizing the false positive rate directly maximizes specificity. False positive rate is FP divided by (FP plus TN). If you reduce the number of false positives while true negatives stay the same, the false positive rate drops and, at the same time, the proportion of true negatives among all those without disease—specificity, which is TN divided by (FP plus TN)—increases. So lowering FP improves specificity, making it the metric that is maximized by minimizing false positives. Sensitivity depends on true positives and false negatives, not on FP in the same direct way. Positive predictive value and negative predictive value depend on disease prevalence and the balance of all test outcomes, so they’re not maximized simply by reducing false positives.