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Activity Number: 79 - Statistical Analysis for Networks
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #327161
Title: Estimating Heterogeneous Biomarker Networks and Their Effects on Disease Outcome
Author(s): Shanghong Xie* and Xiang Li and Donglin Zeng and Yuanjia Wang
Companies: Columbia University and Statistics and Decision Sciences, Janssen Research & Development, LLC and UNC Chapel Hill and Columbia University
Keywords: Graphical model; Regularized regression; Mediation analysis; Gray matter network; White matter connectivity; Huntington's disease

Biomarkers are often organized into networks, in which the strengths of network connections vary across subjects depending on subject-specific covariates. Variation of brain network connections, as subject-specific feature variables, has been found to predict disease clinical outcome in many studies. In this work, we develop a two-stage statistical method to estimate covariate-dependent brain networks to account for heterogeneity among network measures and evaluate their association with disease clinical manifestation. In the first stage, we propose a conditional Gaussian graphical model with mean and precision matrix depending on covariates to obtain subject-specific networks. In the second stage, we model the network connections estimated from the first step jointly with the biomarkers and covariates to identify important features of a clinical outcome via regularized regression. We assess the performance of our proposed method by extensive simulation studies and apply the method to a Huntington's disease (HD) study to investigate the effect of HD causal gene on the rate of change in motor symptom as mediated through brain subcortical and cortical gray matter atrophy connections.

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

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