Understanding likelihood ratios is part of the mastery of a diagnostic work up. Likelihood ratio help explain the significance of each test we perform and the difference they make to our patients.
The area under the curve is used during the development of a diagnostic test. The more accurate the test the better it separates those with the disease and those without it and the area under the curve can be used to quantify this.
This podcast covers
Categories of likelihood ratios; Goodacre 2009 EMJ
|Likelihood ratio||Diagnostic value|
|1||None at all|
|0.5||Little clinical significance|
|2 to 5||Moderately increases likelihood of disease|
|5 to 10||Markedly increases likelihood of disease|
|0.1 to 0.2||Markedly decreases likelihood of disease|
|Less than 0.1||Rules out the disease|
Significance of the area under a curve
|Area under the curve||Significance|
Once you’ve listened help consolidate the topics covered with the questions below.
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Positive and negative likelihood ratios will vary dependant upon the prevalence of the disease in question?
Likelihood ratios can be used to calculate post test probabilities?
The magnitude of a likelihood ratio can be used to determine its significance?
When looking to develop a diagnostic test, the area under the curve can be used to help evaluate the utility of a test. An area under the curve of 1 means that the test will be no more useful than relying on chance.
If a patient has a pretest likelihood of disease of 10%, you apply a test gaining a positive result and you know the test to have a positive likelihood ratio of 5, the post test probability will be 50%?
A positive likelihood ratio is calculated by sensitivity/(1-specificity)?