A BAYESIAN APPROACH FOR SELECTING RELEVANT EXTERNAL DATA (BASE): APPLICATION TO A STUDY OF LONG-TERM OUTCOMES IN A HEMOPHILIA GENE THERAPY TRIAL (HOPE-B)

• 成果类型: Article
• 署名作者: Pan, Tianyu; Shi, Yiyao; Zhang, Xiang; Shen, Weining; Ye, Ting
• 署名单位: Stanford University; University of California System; University of California Irvine; CSL; University of Washington; University of Washington Seattle
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/26-AOAS2181
• 发表日期: 2026-06
• 页码: 1319-1339
• 关键词: Bayesian analysis; data integration; gene therapy; long-term outcome inference; selective borrowing; CLINICAL BENEFIT

摘要

Gene therapies aim to address the root causes of diseases, particularly those stemming from rare genetic defects that can be life-threatening or severely debilitating. Although an increasing number of gene therapies have received regulatory approvals in recent years, understanding their long-term efficacy in trials with limited follow-up time remains challenging. To address this question, we propose a novel Bayesian framework to selectively integrate relevant external data with internal trial data to improve the inference of the durability of long-term efficacy. We proved that the proposed method can theoretically identify external subsets deemed relevant, where relevance is defined as the similarity, induced by the marginal likelihood, between the generating mechanisms of the internal data and the selected external data. We conducted simulations to evaluate its performance under various scenarios. Furthermore, we apply this method to predict and infer the endogenous factor IX (FIX) levels of patients who receive Etranacogene dezaparvovec long term. Our estimated long-term FIX levels, validated by recent trial data, indicate that Etranacogene dezaparvovec induces sustained FIX production. Together, the theoretical findings, simulation results, and application of this framework underscore its potential to address long-term effectiveness estimation and inference questions in real-world applications.