Which statement best describes Likelihood Ratios?

Which statement best describes Likelihood Ratios?

• A. They measure the overall accuracy of a test independent of disease prevalence.
• B. They replace pre-test probability.
• C. They compare post-test probability to pre-test probability.
• D. They express how much more or less likely a disease is after a test result.

Answer: (D)

Likelihood ratios describe how a test result changes the likelihood of disease. They tell you how much more likely a positive result makes the disease, or how much less likely a negative result makes it. This is captured by the two forms: LR+ (how much to increase the odds after a positive result) and LR− (how much to decrease the odds after a negative result). They come from the test’s sensitivity and specificity (LR+ = sensitivity divided by 1 minus specificity; LR− = (1 minus sensitivity) divided by specificity). The key is that you use them with the pre-test probability to get the post-test probability: post-test odds = pre-test odds × LR, and then convert to probability if needed. Because they rely on sensitivity and specificity, likelihood ratios are independent of disease prevalence, making them a robust way to interpret test results across settings.

Likelihood ratios describe how a test result changes the likelihood of disease. They tell you how much more likely a positive result makes the disease, or how much less likely a negative result makes it. This is captured by the two forms: LR+ (how much to increase the odds after a positive result) and LR− (how much to decrease the odds after a negative result). They come from the test’s sensitivity and specificity (LR+ = sensitivity divided by 1 minus specificity; LR− = (1 minus sensitivity) divided by specificity). The key is that you use them with the pre-test probability to get the post-test probability: post-test odds = pre-test odds × LR, and then convert to probability if needed. Because they rely on sensitivity and specificity, likelihood ratios are independent of disease prevalence, making them a robust way to interpret test results across settings.