Bayesian Analysis of Longitudinal Quality of Life Measures with Informative Missing Data Using a Selection Model

• Activity Number: 171 - Missing Data
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Jaeil Ahn*
• Companies: Georgetown University
• Keywords: multivariate; HRQOL; longitudinal; missing data; selection; longitudinal

Abstract

Health related quality of life (HRQOL) consists of multi-dimensional measurements and is often followed up to evaluate efficacy of treatments in clinical studies. During the follow-up period, a missing data problem inevitably arises. We propose a Bayesian approach to analyze longitudinal moderate to high dimensional multivariate outcome data in the presence of non-ignorable missing data. To account for non-ignorable missing data, we employ a selection model for the joint likelihood factorization where we apply Bayesian spike and slab variable selection in the missing data mechanism. We model the relationship between multiple outcomes and covariates using linear mixed effects models where multiple outcome correlations are captured by a hierarchical structure. We conduct simulation studies to evaluate the performance of our method compared with the conventional last observation carried forward approach. We apply our method to a longitudinal study of quality of life in gastric cancer patients where we demonstrate that our proposed method can offer efficiency gain in the marginal associations and provide the associations between outcomes and the absence of patients’ information.