Is likelihood ratio affected by prevalence?

As opposed to predictive values, likelihood ratios are not affected by the disease prevalence and are therefore used to adopt the results from other investigators to your own patient population.

What does positive likelihood ratio mean?

Positive LR: This tells you how much to increase the probability of having a disease, given a positive test result. The ratio is: Probability a person with the condition tests positive (a true positive) / probability a person without the condition tests positive (a false positive).

Is PPV affected by prevalence?

As the prevalence increases, the PPV also increases but the NPV decreases. Similarly, as the prevalence decreases the PPV decreases while the NPV increases.

Does positive predictive value depend on prevalence?

Predictive values are based upon the prevalence of disease in a population. As the prevalence of disease decreases, the positive predictive value decreases. In the general population, few diseases reach a prevalence of 1%.

What does positive and negative likelihood ratio mean?

The interpretation of likelihood ratios is intuitive: the larger the positive likelihood ratio, the greater the likelihood of disease; the smaller the negative likelihood ratio, the lesser the likelihood of disease.

When do you use a positive or negative likelihood ratio?

The more the likelihood ratio for a positive test (LR+) is greater than 1, the more likely the disease or outcome. The more a likelihood ratio for a negative test is less than 1, the less likely the disease or outcome. Thus, LRs correspond nicely to the clinical concepts of ruling in and ruling out disease.

What does a positive predictive value of 50% mean?

A likelihood ratio of 50 means that the post test odds of disease for a positive test result will be 50 times higher than the pretest odds of disease.

Why does PPV increase with prevalence?

Positive and negative predictive values are influenced by the prevalence of disease in the population that is being tested. If we test in a high prevalence setting, it is more likely that persons who test positive truly have the disease than if the test is performed in a population with low prevalence.

Why the positive predictive value depends on the prevalence of the disease in the population being screened?