Which statistic best indicates how much a test result changes the likelihood of disease?

Which statistic best indicates how much a test result changes the likelihood of disease?

• A. Sensitivity
• B. Likelihood Ratios
• C. Specificity
• D. Prevalence

Answer: (B)

The idea being tested is how a test result changes the probability that someone has the disease. Likelihood ratios do this directly. They quantify how much more (or less) likely a given test result is in someone with the disease compared to someone without it. A positive test result increases the odds of disease by the positive likelihood ratio, while a negative result reduces the odds by the negative likelihood ratio. Mathematically, you apply Bayes’ rule: post-test odds = pre-test odds × likelihood ratio. The pre-test odds come from the prior probability (often the disease prevalence in the relevant setting), so likelihood ratios bridge the test result and the individual’s updated probability. Sensitivity and specificity describe how well the test performs across groups, but they don’t tell you how a specific result changes a person’s probability. Prevalence is the baseline chance of disease in a population, not the update after testing.

The idea being tested is how a test result changes the probability that someone has the disease. Likelihood ratios do this directly. They quantify how much more (or less) likely a given test result is in someone with the disease compared to someone without it. A positive test result increases the odds of disease by the positive likelihood ratio, while a negative result reduces the odds by the negative likelihood ratio. Mathematically, you apply Bayes’ rule: post-test odds = pre-test odds × likelihood ratio. The pre-test odds come from the prior probability (often the disease prevalence in the relevant setting), so likelihood ratios bridge the test result and the individual’s updated probability.

Sensitivity and specificity describe how well the test performs across groups, but they don’t tell you how a specific result changes a person’s probability. Prevalence is the baseline chance of disease in a population, not the update after testing.