Advances in Bayesian Hierarchical Models for Complex Health Data

Abstract

With the advancement of technology in screening and tracking risk factors as well as human health outcomes, there is increasing richness and complexity in health data. This dissertation presents methodological and applied work using Bayesian hierarchical models to exploit dependency structure in the data to improve estimation efficiency, and sometimes also reduce computational cost and increase interpretability. In Chapter 2, we present a multivariate factor analysis model with time-varying effects to assess the longitudinal effects of prenatal exposure to phthalates on the risk of childhood obesity in children aged 4 to 10. In Chapter 3, we present a framework and package for power analysis using Monte Carlo simulation for study design as well as model comparison of complex models for correlated chemical mixture exposure data. In Chapter 4, we introduce a new way to characterize bias due to unmeasured confounding using a set of imperfect negative control outcomes, taking advantage of the knowledge that they share common unobserved causes. Finally, in Chapter 5, we present a new tree representation of brain connectomes based on the biological hierarchy of brain regions. In all these applications, we use Bayesian hierarchical models for borrowing information across related observations and enforcing latent structures.