Fitting binary regression models with case-augmented samples

• 成果类型: Article
• 署名作者: Lee, A. J.; Scott, A. J.; Wild, C. J.
• 署名单位: University of Auckland
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/93.2.385
• 发表日期: 2006
• 页码: 385397
• 关键词: design

摘要

In a case-augmented study, measurements on a random sample from a population are augmented by information from an independent sample of cases, that is units with some characteristic of interest. We show that inferences about the effect of the covariates on the probability of being a case can be made by fitting a modified prospective likelihood. We also show that this procedure is fully efficient.