Modelling Correlated Bivariate Binary Data: A Comparative View

This study focused on comparing selected commonly used marginal models with marginal-conditional models for analyzing correlated longitudinal binary data. A simulation study shows that for explaining the relationship among the covariates and the repeated outcomes, each of the proposed models show competitive results in terms of bias and coverage probability as compared to the marginal models. If the repeated outcomes are associated or if the distribution of outcome variables are not identical at different follow-ups, the marginal-conditional models give better results in terms of bias and coverage probability of the estimates. For keeping the number of parameters to be estimated as small as possible, the regressive model is suggested for data with more than three follow-ups. The methods are illustrated with an example using Health and Retirement Study data.