Regression Analysis for Differentially Misclassified Correlated Binary Outcomes.

In many epidemiological and clinical studies, misclassification may arise in one or several variables, resulting in potentially invalid analytic results (e.g., estimates of odds ratios of interest) when no correction is made. Here we consider the situation in which correlated binary response variables are subject to misclassification. Building upon prior work, we provide an approach to adjust for potentially complex differential misclassification via internal validation sampling applied at multiple study time points. We seek to estimate the parameters of a primary generalized linear mixed model (GLMM) that accounts for baseline and/or time-dependent covariates. The misclassification process is modeled via a second generalized linear model that captures variations in sensitivity and specificity parameters according to time and a set of subject-specific covariates that may or may not overlap with those in the primary model. Simulation studies demonstrate the precision and validity of the proposed method. An application is presented based on longitudinal assessments of bacterial vaginosis conducted in the HIV Epidemiology Research (HER) Study.