A marginal regression modelling framework for evaluating medical diagnostic tests.

Technological advances continue to develop for early detection of disease. Research studies are required to define the statistical properties of such screening or diagnostic tests. However, statistical methodology currently used to evaluate diagnostic tests is limited. We propose the use of marginal regression models with robust sandwich variance estimators to make inference about the sensitivity and specificity of diagnostic tests. This method is more flexible than standard methods in that it allows comparison of sensitivity between two or more tests even if all tests are not carried out on all subjects, it can accommodate correlated data, and the effect of covariates can be evaluated. This last feature is important since it allows researchers to understand the effects on sensitivity and specificity of various environmental and patient characteristics. If such factors are under the control of the clinician, it provides the opportunity to modify the diagnostic testing program to maximize sensitivity and/or specificity. We show that the marginal regression modelling methods generalize standard statistical methods. In particular, when we compare two screening tests and we test each subject with both screens, the method corresponds to McNemar's test. We describe data from an ongoing audiology screening study and we analyse a simulated version of the data to illustrate the methodology. We also analyse data from a longitudinal study of PCR as a diagnostic test for cytomegalovirus.