Kiros Berhane (Keck School of Medicine, University of Southern California)

Functional-based Multi-level Modeling of Multiple Longitudinal Outcomes: with applications to environmental epidemiology

Flexible multi-level models are proposed to allow for cluster specific smooth estimation of growth curves, in a mixed-effects modeling format that includes subject-specific random effects on the growth parameters. Attention is then focused on models that examine between-cluster comparisons of the effects of an ecologic covariate of interest (e.g., air pollution) on nonlinear functionals of growth curves (e.g. maximum rate of growth). A Gibbs sampling approach is used to get posterior mean estimates of nonlinear functionals along with their uncertainty estimates. A second-stage ecologic random effects model is used to examine the association between a covariate of interest (e.g., air pollution) and the nonlinear functionals. A unified estimation procedure is presented along with its computational and theoretical details. This work is further extended to allow for modeling of multiple outcomes via a latent variable approach in order to connect several outcomes from a subject. The models are motivated by, and illustrated with, lung function, asthma and air pollution data from the Southern California Children's Health Study.