Quadratic inference function approach to merging longitudinal studies: validation and joint estimation

• 成果类型: Article
• 署名作者: Wang, Fei; Wang, Lu; Song, Peter X. -K.
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass021
• 发表日期: 2012
• 页码: 755762
• 关键词: GENERALIZED ESTIMATING EQUATIONS; robust variance estimator; models; components; tests

摘要

Merging data from multiple studies has been widely adopted in biomedical research. In this paper, we consider two major issues related to merging longitudinal datasets. We first develop a rigorous hypothesis testing procedure to assess the validity of data merging, and then propose a flexible joint estimation procedure that enables us to analyse merged data and to account for different within-subject correlations and follow-up schedules in different studies. We establish large sample properties for the proposed procedures. We compare our method with meta analysis and generalized estimating equations and show that our test provides robust control of Type I error against both misspecification of working correlation structures and heterogeneous dispersion parameters. Our joint estimating procedure leads to an improvement in estimation efficiency on all regression coefficients after data merging is validated.