A Comparison of Three Models in Multivariate Binary Longitudinal Data Analysis: Application to FDCS Study

Abstract

Multivariate longitudinal data analysis plays an important role in many biomedical and social problems. In this article, we present three methods for analyzing multiple and correlated binary outcomes; each one can be beneficial for determined aims. We review methods one and two, and we proposed method three. The three methods estimate the marginal means using the GEE approach for multivariate binary longitudinal data. The first method addresses the question of estimating one group of covariate parameters for many binary outcomes while accounting for their multivariate structure. The second method addresses the question of estimating the covariate parameters for each binary outcome separately. The third method is an estimation of the covariate parameters for each combination of outcomes. Our goal is to investigate the differences among the parameter estimations of the three methods. In the application element, we present a follow-up study (Florida Dental Care Study) that measured three binary outcomes and five covariates at four intervals. The FDCS study is useful explanation of the variation between outcomes since the outcomes were highly correlated.