A Random Effects Hierarchical Model with Bayes Prior

Oregon Convention Center 

Longitudinal studies and repeated measure are key in the study of correlated data. Irimata, Wilson 2017 presented a measure of these correlations when measuring the strength of association between an outcome of interest and multiple binary outcomes, as well as the clustering present due to correlation. They addressed the set of correlation in a hierarchical model with random effects. Estimation of parameters in such models is hampered by the association between time dependent binary variables and the outcome of interest. Wilson, Vazquez, Chen 2020 described marginal models in the analysis of correlated binary data with time dependent covariates. Their research addressed carryover effects on covariates and covariates unto responses through marginal models.
This research uses a random effects model with multiple outcomes to account for the changing impact of responses on covariates and covariates on response. It requires a series of distributions to address time-dependent covariates, as each random effect relies on a distribution. It differs from that of Wilson, Vazquez, Chen with their marginal models but is based on random effects in modeling (conditional model) feedback effects.

Longitudinal studies

Correlation

Binary Models 

Health Policy Statistics Section