Regression modelling of diagnostic likelihood ratios for the evaluation of medical diagnostic tests.

The use of diagnostic likelihood ratios has been advocated in the epidemiologic literature for the past decade. Diagnostic likelihood ratios provide valuable information about the predictive properties of a diagnostic test while having the attractive feature of being independent of the prevalence of disease in the study population. We propose a new regression method that allows for direct assessment of covariate effects on likelihood ratios for binary diagnostic tests. This may be particularly useful in assessing how factors that are under the control of the clinician can be altered to maximize the predictive ability of the test. Similarly, patient characteristics that influence the ability of the test to discriminate between diseased and nondiseased subjects may be identified using the regression model. The regression method is flexible in that it can accommodate clustered data arising from a variety of study designs. We illustrate the method with data from an audiology study.