Title: BAYESIAN ANALYSIS OF LONGITUDINAL DYADIC DATA WITH INFORMATIVE MISSING DATA
Abstract: Longitudinal dyadic data with missing data is difficult to analyze due to the complicated inter-and outer correlations within and between dyads, as well as non-ignorable missing data. In this talk, I will introduce 1) a Bayesian mixed effects hybrid model to analyze longitudinal data with non-ignorable dropouts and 2)a Bayesian shared parameter model to analyze longitudinal dyadic data with non-ignorable intermittent dropouts. I factorize the joint distribution of the measurement and dropout processes into three components where the two approaches use different factorizations. The two proposed models account for the dyadic interplay using the concept of actor and partner effects as well as dyad-specific random effects. We evaluate the performance of the proposed methods using a simulation study, and apply our method to longitudinal dyadic datasets that arose from 1) a prostate cancer trial and 2) a metastatic breast cancer study. Through a series of sensitivity analyses, we can provide validity of the proposed model.