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

Abstract Number:

3236 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Soumya Sahu (1), Sanjib Basu (1), Jiehuan Sun (1), Joelle Hallak (2)

Institutions:

(1) University of Illinois Chicago, Chicago, IL, (2) Ophthalmology, Illinois Eye and Ear Infirmary, University of Illinois Chicago; AbbVie, Chicago, IL

Co-Author(s):

Sanjib Basu  
University of Illinois Chicago
Jiehuan Sun  
University of Illinois Chicago
Joelle Hallak  
Ophthalmology, Illinois Eye and Ear Infirmary, University of Illinois Chicago; AbbVie

First Author:

Soumya Sahu  
University of Illinois Chicago

Presenting Author:

Soumya Sahu  
N/A

Abstract Text:

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.

Keywords:

Joint Modeling |High-dimensional|Functional modeling|Bayesian Non-parametrics|Age related Macular degeneration (AMD)|Ophthalmology

Sponsors:

Section on Statistics in Imaging

Tracks:

Imaging

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