主  題：A Joint Modeling Approach for Treatment Response and Baseline Imaging Data

內容簡介：There have been a lot of interests in using neuroimaging approaches to help guide clinicians in selecting treatment for patients of major depressive disorders and understand placebo response. In this talk, we discuss a unified Bayesian framework for modeling treatment outcome while exploiting
image-based features as predictors. Traditional methods usually take two steps. First, a dimension reduction procedure is conducted to reduce high- dimensional images to low dimensional features. Second, a regression analysis is carried out to investigate the relationship between a treatment response of interest and the extracted low dimensional imaging features such that the effect of treatment depends on these imaging features. In contrast, our method performs these tasks simultaneously to ultimately take into account uncertainty in both steps. We also illustrate the application of the method on a large placebo-controlled depression clinical trial using baseline EEG measurements. This is a joint work with Eva Petkova, Thaddeus Tarpey and R. Todd Ogden.

報告人：Bei Jiang  博導

時  間：2016-12-23    10:10

地  點：競慧東樓305

舉辦單位：理學院 統計學與大數據研究院 科研部