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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...
Joint modeling methods have become popular tools to link important features extracted from longitudinal data to a primary event. While most modeling strategies have focused on the association between the longitudinal mean trajectories and risk of an event, we consider joint models that incorporate information from both long‐term trends and short‐term variability in a longitudinal submodel. We also...
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