Accounting for Response Misclassification and Covariate Measurement Error Using a Random Effects Logit Model - Roy, Surupa (Communications in statistics. Simulation and computation, 2012)
Often in longitudinal data arising out of epidemiologic studies, measurement error in covariates and/or classification errors in binary responses may be present. The goal of the present work is to develop a random effects logistic regression model that corrects for the classification errors in binary responses and/or measurement error in covariates. The analysis is carried out under a Bayesian set up. Simulation study reveals the effect of ignoring measurement error and/or classification errors on the estimates of the regression coefficients.