Which category includes diagnostic accuracy metrics such as sensitivity, specificity, and likelihood ratios?

Which category includes diagnostic accuracy metrics such as sensitivity, specificity, and likelihood ratios?

• A. Sensitivity, Specificity, and Likelihood Ratios
• B. Mean and Median
• C. Correlation Coefficients
• D. Confidence Intervals

Answer: (A)

Diagnostic accuracy metrics describe how well a test distinguishes those with disease from those without. Sensitivity is the proportion of true positives among people who truly have the disease, showing how good the test is at catching disease. Specificity is the proportion of true negatives among people without the disease, showing how good the test is at ruling out disease. Likelihood ratios combine sensitivity and specificity to indicate how a positive or negative result changes the odds of disease. The positive likelihood ratio tells you how much more likely a positive result is in someone with disease than in someone without it; the negative likelihood ratio tells you how much less likely a negative result is in someone with disease than in someone without it. Using likelihood ratios to update pre-test probability to post-test probability (via Bayes’ theorem) makes these metrics practical for clinical decision-making. These measures are all about evaluating test performance, not about describing central tendency, associations between variables, or precision of an estimate, which is why they fit together under diagnostic accuracy metrics.

Diagnostic accuracy metrics describe how well a test distinguishes those with disease from those without. Sensitivity is the proportion of true positives among people who truly have the disease, showing how good the test is at catching disease. Specificity is the proportion of true negatives among people without the disease, showing how good the test is at ruling out disease. Likelihood ratios combine sensitivity and specificity to indicate how a positive or negative result changes the odds of disease. The positive likelihood ratio tells you how much more likely a positive result is in someone with disease than in someone without it; the negative likelihood ratio tells you how much less likely a negative result is in someone with disease than in someone without it. Using likelihood ratios to update pre-test probability to post-test probability (via Bayes’ theorem) makes these metrics practical for clinical decision-making. These measures are all about evaluating test performance, not about describing central tendency, associations between variables, or precision of an estimate, which is why they fit together under diagnostic accuracy metrics.