Functional hybrid factor regression model for handling heterogeneity in imaging studies

• 成果类型: Article
• 署名作者: Huang, C.; Zhu, H.
• 署名单位: State University System of Florida; Florida State University; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asac007
• 发表日期: 2022
• 页码: 11331148
• 关键词: alzheimers-disease; neuroimaging data; metaanalysis; multisite; RISK; jive; mri

摘要

This paper develops a functional hybrid factor regression modelling framework to handle the heterogeneity of many large-scale imaging studies, such as the Alzheimer's disease neuroimaging initiative study. Despite the numerous successes of those imaging studies, such heterogeneity may be caused by the differences in study environment, population, design, protocols or other hidden factors, and it has posed major challenges in integrative analysis of imaging data collected from multicentres or multistudies. We propose both estimation and inference procedures for estimating unknown parameters and detecting unknown factors under our new model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed procedures is assessed by using Monte Carlo simulations and a real data example on hippocampal surface data from the Alzheimer's disease study.