Calculate post-test probability using Bayes' theorem
Enter an estimate of the likelihood the condition exists prior to testing
A Likelihood Ratio tells you how much a test result changes the probability that a condition exists. Unlike sensitivity and specificity alone, likelihood ratios combine both measures into a single number that directly indicates a test's diagnostic power.
The Positive Likelihood Ratio represents how many times more likely a positive test result is in someone who has the condition compared to someone who does not. A Likelihood Ratio (+) of 10, for example, means a positive result is 10 times more likely to occur in a person with the disease than in a healthy person. The higher this number, the more powerfully a positive result rules in the condition. Values above 10 provide strong diagnostic evidence, values between 5 and 10 are moderately useful, and values below 2 provide little meaningful shift in probability.
The Negative Likelihood Ratio works in reverse. It represents how likely a negative test result is in someone with the condition compared to someone without it. A Likelihood Ratio (-) of 0.1 means a negative result is 10 times less likely in a person who actually has the disease, making it strong evidence against the condition being present. The closer to zero, the more confidently a negative result rules out the disease. Values below 0.1 provide strong evidence for ruling out, values between 0.1 and 0.2 are moderately useful, and values above 0.5 offer little reassurance.
A Likelihood Ratio of exactly 1 (positive or negative) means the test result does not change the probability at all — the test provides no diagnostic information in that direction. The further the value is from 1, the more the test result shifts your confidence about whether the condition is present.