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Activity Number: 603 - Statistical Inference for Precision Medicine and Subgroup Analysis
Type: Invited
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #326618
Title: Bayesian Variable Selection in Subgroup Analysis
Author(s): Juan Shen* and Naveen Naidu Narisetty and Xuming He
Companies: Fudan University and University of Illinois at Urbana Champaign and University of Michigan
Keywords: subgroup; mixture model; Bayesian; variable selection

In recent years, subgroup analysis has emerged as an important task due to inherent heterogeneity of the subjects in clinical trials and market segmentation analysis. However, most of the existing work for subgroup analysis has considered a small number of candidate variables for characterizing the subgroup membership. In the data-rich era, the candidate variables could be high dimensional such as genetic data of patients in clinical trials. In this talk, we consider a high dimensional mixture model to jointly capture the subgroup membership as well as the within-subgroup behavior and develop a Bayesian variable selection method in that framework. We investigate the theoretical and empirical properties of the proposed method, and suggest how model-based inference may be carried out in subgroup analysis.

Authors who are presenting talks have a * after their name.

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