BIVARIATE HIERARCHICAL BAYESIAN MODEL FOR COMBINING SUMMARY MEASURES AND THEIR UNCERTAINTIES FROM MULTIPLE SOURCES

• 成果类型: Article
• 署名作者: Yao, Yujing; Ogden, R. Todd; Zeng, Chubing; Chen, Qixuan
• 署名单位: Columbia University; University of Southern California
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1699
• 发表日期: 2023
• 页码: 1782-1800
• 关键词: small-area estimation; diagnostic-test; metaanalysis; shrinking; specificity; sensitivity; error

摘要

It is often of interest to combine available estimates of a similar quantity from multiple data sources. When the corresponding variances of each esti-mate are also available, a model should take into account the uncertainty of the estimates themselves as well as the uncertainty in the estimation of vari-ances. In addition, if there exists a strong association between estimates and their variances, the correlation between these two quantities should also be considered. In this paper we propose a bivariate hierarchical Bayesian model that jointly models the estimates and their estimated variances, assuming a correlation between these two measures. We conduct simulations to explore the performance of the proposed bivariate Bayesian model and compare it to other commonly used methods under different correlation scenarios. The proposed bivariate Bayesian model has a wide range of applications. We illus-trate its application in three very different areas: PET brain imaging studies, meta-analysis, and small area estimation.