Bayesian estimation of varying-coefficient models with missing data, with application to the Singapore Longitudinal Aging Study

Abstract

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

Keywords:

• Bayesian nonparametric estimation
• Bayesian multiple-imputation
• longitudinal data
• missing data
• varying-coefficient model

Funding

This work is supported by grants from Academic Research Funding [R-155-000-130-112] and National Medical Research Council [NMRC/CBRG/0014/2012].