Using covariate-specific disease prevalence information to increase the power of case-control studies
提出一种利用协变量特定疾病患病率辅助信息的统计方法，通过经验似然估计提高病例对照研究中逻辑回归模型的效率，并通过模拟和实际应用验证其优势。
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.