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 (MPCA) 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 (PCR) in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline EEG data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods. This article is protected by copyright. All rights reserved.

中文翻译：

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矩阵值成像数据和治疗结果联合建模的贝叶斯方法及其在抑郁症研究中的应用


在本文中，我们提出了一个统一的贝叶斯联合建模框架，用于研究二元治疗结果和基线矩阵值预测变量之间的关联。具体来说，开发了一种联合建模方法，通过多线性主成分分析（MPCA）的概率公式将结果与矩阵值预测变量相关联。尽管预测变量没有在模型中明确表达，但该框架在结果和矩阵值预测变量之间建立了理论关系。仿真研究表明，该方法在预测精度和关联估计方面优于或优于其他方法，例如两阶段方法和经典主成分回归（PCR）；当样本量较小且成像协变量的维数较大时，其优势最为显着。最后，我们提出的联合建模方法被证明是一种非常有前途的工具，在探索基线脑电图数据与抑郁症治疗研究中对治疗的有利反应之间的关联的应用中，与竞争方法相比，预测准确性得到了显着提高。本文受版权保护。版权所有。