Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation

• 成果类型: Article
• 署名作者: Yang, Shu; Gao, Chenyin; Zeng, Donglin; Wang, Xiaofei
• 署名单位: North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill; Duke University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkad017
• 发表日期: 2023
• 页码: 575-596
• 关键词: generalizing evidence; inference; probability; parameters

摘要

We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data. We characterise the asymptotic distribution of the test-based estimator under local alternatives. We provide a data-adaptive procedure to select the test threshold that promises the smallest mean square error and an elastic confidence interval with a good finite-sample coverage property.