Bayesian Varying-Coefficient Model with Missing Data

Abstract

Motivated by Singapore Longitudinal Aging Study (SLAS), we propose a Bayesian approach for the estimation of semiparametric varying-coefficient models for longitudinal normal and cross-sectional binary responses. These models have proved to be more flexible than simple parametric regression models, and our Bayesian solution eases the computation complexity of these models. We also consider adapting all kinds of familiar statistical strategies to address the missing data issue in SLAS. Our simulation results indicate that Bayesian imputation approach performs better than complete-case and available-case approaches, especially under small sample designs, and may provide more useful results in practice. In the real data analysis for SLAS, the results from Bayesian imputation are similar to available-case analysis, differing from those with complete-case analysis.