Abstract:
Clustered binary data are ubiquitous in many diagnostic studies in medicine
and health. This is true in situations where the same group of readers
evaluates the presence or absence of certain diseases on binocular organs.
Note the complex correlation structure in the data: in addition to the
correlation induced by the binocular nature of data one other source of
correlation is present. Since readers rely on same patients’ result, their
diagnoses are potentially correlated. The later correlation can be accounted by
incorporating reader-specific and patient-specific random effects. These
random effects are crossed rather than nested. Hence, the evaluation of full
likelihood is cumbersome since the integral suffers from the curse of
dimensionality and integral increases with the number of patients and readers.
In this study, a likelihood-based method of estimating disease-specific
sensitivities and specificities via generalized linear mixed models with copula
to capture the binocular correlation is proposed. To overcome the
computational complexities in the likelihood, pairwise likelihood approach is
adopted. An underlying latent t- distribution is assumed for binary
observations; this is robust to the conventional probit and logistic regression
models. Data from a study on diabetic retinopathy are analyzed to illustrate
the methodology.
