Final answer:
A negative likelihood ratio indicates how much less likely a disease is after a negative test result. An LR- less than one suggests the condition is less likely, and values significantly lower than 1 are especially useful for ruling out a disease or condition.
Step-by-step explanation:
A negative likelihood ratio (LR-) tells us how much the odds of the disease decrease when a test is negative. In epidemiology, the LR- is used to determine the diagnostic usefulness of a test. A likelihood ratio less than one indicates that a negative test result is associated with the absence of the condition being tested for. The lower the negative likelihood ratio, the less likely it is that a person has the disease or condition after a negative test result. In traditional statistical analyses, a low likelihood ratio may also signify the rarity of an event.
To explain further, if the LR- is significantly less than 1, it means that a negative test result is a good indicator that the disease is not present. For example, if the negative likelihood ratio is 0.1, a negative test result would make the disease 10 times less likely. In contrast, an LR- close to 1 does not change the odds of disease much. Epidemiologists and clinicians find negative likelihood ratios below 0.1 as highly useful for ruling out a condition (often summarized as 'SnNout' - when a sign/test with a high Sensitivity is Negative, it rules OUT the disease).