Using covariate-specific disease prevalence information to increase the power of case-control studies

• 成果类型: Article
• 署名作者: Qin, Jing; Zhang, Han; Li, Pengfei; Albanes, Demetrius; Yu, Kai
• 署名单位: National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; University of Waterloo
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu048
• 发表日期: 2015
• 页码: 169180
• 关键词: genome-wide association; lung-cancer; susceptibility locus; Empirical Likelihood; models; variants; 5p15.33

摘要

Public registration databases and large cohort studies provide vital information on disease prevalence at various levels of a risk factor. This auxiliary information can be helpful in conducting statistical inference in a new study. We aim to develop a statistical procedure that improves the efficiency of the logistic regression model for a case-control study by utilizing auxiliary information on covariate-specific disease prevalence via a series of unbiased estimating equations. We adopt empirical likelihood for statistical inference, and demonstrate its advantages through simulation and an application.