Post date: Apr 23, 2018 3:48:21 PM
Title : BAYESIAN ANALYSIS OF LONGITUDINAL DYADIC/MULTIPLE OUTCOME DATA WITH INFORMATIVE MISSING DATA
Speaker: Jaeil Ahn, Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University
Abstract:
Longitudinal dyadic/multiple outcome data with missing data are difficult to analyze due to the complicated inter-and outer correlations within and between dyads and multiple outcomes, as well as non-ignorable missing data. In the first part of the talk, I will introduce a Bayesian mixed effects hybrid model to analyze longitudinal dyadic data with non-ignorable dropouts/intermittent dropouts. I factorize the joint distribution of the measurement, random effects, and dropout processes into three components. The proposed model accounts for the dyadic interplay using the concept of actor and partner effects as well as dyad-specific random effects. I evaluate the performance of the proposed methods using a simulation study, and apply our method to longitudinal dyadic datasets that arose from a prostate cancer trial. In the second part of the talk, I will introduce a Bayesian mixed effects selection model to analyze longitudinal quality of life data with non-ignorable missing data. Compared to the first model, I 1) describe the overall/local effects of predictors on outcomes simultaneously and 2) incorporate a variable selection feature in the missing data mechanism to account for the impact of potentially moderate to high dimensional outcomes on missing data mechanisms.