GENERATING MULTIVARIATE LONGITUDINAL BINARY RANDOM VARIABLES FOR GEE MODELS USING BRIDGE DISTRIBUTION.

Generalized estimating equations (GEE) models are often used to analyze the longitudinal data.It accounts for the within-subject associations through specification of working correlation matrixR. In multivariate longitudinal data, the within-subject correlation is computed by many outcomesare measured over many occasions. Then, the correlation is the main problem in the multivariate longitudinal data. This complicated correlation may affect the parameter estimations precision when it is increased over the outcomes or occasions. Designing a simulation method to investigate the correlation effects on the parameter estimations for the marginal models could be good statistical tool in the longitudinal data analysis. In this paper, we utilize a method to generate correlated binary data for a multivariate longitudinal model with specified R correlation matrix. This specified structure allows the correlation to be induced over the outcomes or occasions. We utilized the methods of Wang and Louis (2003) and Parzen et al. (2011) to use the generalized linear mixed models via a bridge distribution to generate multivariate binary longitudinal data for marginal models. In addition, we conducted a clinical trial simulation study for analyzing multiple and correlated binary outcomes based on control the correlation over the outcomes and occasions, and estimate the effect sample size. This approach could be a good method in simulating the correlated binary data. We include an explanation of some constraints to achieving the best simulation results.