Population-average and subject-specific approaches for the analysis of misclassified correlated binary outcomes with internal validation.

Misclassification of correlated binary responses may occur in clinical and epidemiological studies, resulting in biased and/or inefficient parameter estimation. We extend existing generalized estimating equation (GEE) approaches to allow for differential misclassification via two sets of estimating equations for the analysis of error-prone correlated binary outcomes when internal validation data are available. One set of estimating equations uses logistic regression to model the misclassification process with the internal validation data, using subject characteristics to estimate the differential sensitivities and specificities of the error-prone response in relation to the gold standard. The second set of estimating equations models the mismeasured binary response by leveraging the subject-specific estimates of sensitivity and specificity. The subject-specific covariates need not be identical in the two sets of estimating equations. We present analysis of longitudinal assessments of bacterial vaginosis from the HIV Epidemiology Research Study (HERS), compare the proposed population-average approach based on GEE with a subject-specific one based on a full-likelihood mixed-effects analysis, and discuss the differing parameter interpretations. We also present results from simulated data with a misclassified binary outcome analyzed with this GEE approach.