Which statement best describes likelihood ratio for a negative test result?

Which statement best describes likelihood ratio for a negative test result?

• A. Negative result is strong evidence against disease
• B. Positive result is strong evidence against disease
• C. Negative result is strong evidence for disease
• D. Positive result is strong evidence for disease

Answer: (A)

Likelihood ratios show how much a test result shifts the odds of having disease. When the result is negative, the relevant value is the LR-; a small LR- means the negative result greatly lowers the probability of disease. So saying that a negative result is strong evidence against disease captures the idea that a negative test can rule out disease if the test has good sensitivity and specificity (low LR-). For example, if the pre-test probability is modest and LR- is around 0.1 or smaller, a negative result can reduce the post-test probability to a small percentage, making disease unlikely. Conversely, a positive result is what tends to provide evidence for disease (high LR+), and a negative result would not be described as evidence for disease.

Likelihood ratios show how much a test result shifts the odds of having disease. When the result is negative, the relevant value is the LR-; a small LR- means the negative result greatly lowers the probability of disease. So saying that a negative result is strong evidence against disease captures the idea that a negative test can rule out disease if the test has good sensitivity and specificity (low LR-).

For example, if the pre-test probability is modest and LR- is around 0.1 or smaller, a negative result can reduce the post-test probability to a small percentage, making disease unlikely. Conversely, a positive result is what tends to provide evidence for disease (high LR+), and a negative result would not be described as evidence for disease.