TARGETING UNDERREPRESENTED POPULATIONS IN PRECISION MEDICINE: A FEDERATED TRANSFER LEARNING APPROACH

• 成果类型: Article
• 署名作者: Li, Sai; Cai, Tianxi; Duan, Rui
• 署名单位: Renmin University of China; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1747
• 发表日期: 2023
• 页码: 2970-2992
• 关键词: obesity; association; FRAMEWORK; genomics; records

摘要

The limited representation of minorities and disadvantaged populations in large-scale clinical and genomics research poses a significant barrier to translating precision medicine research into practice. Prediction models are likely to underperform in underrepresented populations due to heterogeneity across populations, thereby exacerbating known health disparities. To ad -dress this issue, we propose FETA, a two-way data integration method that leverages a federated transfer learning approach to integrate heterogeneous data from diverse populations and multiple healthcare institutions, with a focus on a target population of interest having limited sample sizes. We show that FETA achieves performance comparable to the pooled analysis, where individual-level data is shared across institutions, with only a small number of communications across participating sites. Our theoretical analysis and simulation study demonstrate how FETA's estimation accuracy is influenced by communication budgets, privacy restrictions, and heterogeneity across populations. We apply FETA to multisite data from the electronic Medical Records and Genomics (eMERGE) Network to construct genetic risk prediction models for extreme obesity. Compared to models trained using target data only, source data only, and all data without accounting for population-level differences, FETA shows superior predictive performance. FETA has the potential to improve estimation and prediction accuracy in underrepresented popula-tions and reduce the gap in model performance across populations.