High-dimensional Bayesian Semiparametric Functional Joint Model and a Global-Local Selection

Oregon Convention Center 

Current literature on joint models can typically jointly analyze one or a few longitudinal processes and a time-to-event outcome. We develop a Bayesian semiparametric functional joint model that(1)models high-dimensional longitudinal processes with identifying trajectory based on latent classes nested within each process,(2)provides flexibility in modeling the association between the longitudinal processes and time-to-event outcome,and(3)addresses selection from the high-dimensional longitudinally processes in a global-local way where processes are selected globally and a local selection is used to select the effects of latent classes within each process. This work is motivated by high-dimensional imaging features of the eye, measured longitudinally at multiple visits of patients with early-stage age-related macular degeneration(AMD). A primary scientific question is selection of longitudinal feature processes that can prognosticate conversion to neovascular AMD. Our simultaneous analysis of all imaging features in the proposed model highlights unique features associated in multiple ways with prognostication of conversion to neovascular AMD that are distinct from previous findings.

Joint Modeling

High-dimensional

Functional modeling

Bayesian Non-parametrics

Age related Macular degeneration (AMD)

Ophthalmology 

Section on Statistics in Imaging