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Title: A joint modeling approach for baseline matrix-valued imaging data and treatment outcome Authors:  Bei Jiang - University of Alberta (Canada) [presenting]
Abstract: A unified Bayesian joint modeling framework is proposed for studying association between a binary treatment outcome and a baseline matrix-valued predictor, such as imaging data. Under this framework, a theoretically implied relationship can be established between the treatment outcome and the matrix-valued imaging data, although the imaging data is not directly considered in the model. The proposed joint modeling approach provides a promising framework for both association estimation and prediction. Properties of this method are examined using simulated datasets. In particular, our simulations show good performance of the proposed method under even difficult scenarios in which the sample size is small and/or the signal-to-noise (STN) in the imaging data is poor. Finally, a detailed illustration of the proposed modeling approach is provided using a motivating depression study that aims to explore the association between the baseline EEG data and the probability of a favorable response to an antidepressant treatment.