A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies.

ABSTRACT: In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most

SUBMITTER: Jiang B 

PROVIDER: S-EPMC7067625 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature