Bayesian Nonparametric High-Dimensional Joint Modeling

Music City Center 

We consider the setting of joint modeling of a time-to-event outcome and a large number of longitudinally measured processes which are posited to prognosticate the outcome. The literature on joint modeling is diverse; however, current approaches can typically jointly analyze one or a few longitudinal processes. The motivating application for this work comes from a study on Age-related Macular Degeneration (AMD), a disease that affects 12.6% of the US adults aged ≥ 40 years (19.6 million) and more than 190 million people globally. We propose a nonparametric Bayesian joint model for the time-to-event and high-dimensional longitudinal processes that uses flexible low-dimensional structures. We evaluate performance of the proposed approach in simulation studies.

AMD

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