Quantile Regression in the Secondary Analysis of Case–Control Data
提出一种基于分位数的方法，利用病例对照数据研究风险因素对次要结局多个分位数的影响，通过构建新的估计方程实现一致估计，并用哮喘遗传关联研究示例。
Case-control design is widely used in epidemiology and other fields to identify factors associated with a disease. Data collected from existing case-control studies can also provide a cost-effective way to investigate the association of risk factors with secondary outcomes. When the secondary outcome is a continuous random variable, most of the existing methods focus on the statistical inference on the mean of the secondary outcome. In this paper, we propose a quantile-based approach to facilitating a comprehensive investigation of covariates' effects on multiple quantiles of the secondary outcome. We construct a new family of estimating equations combining observed and pseudo outcomes, which lead to consistent estimation of conditional quantiles using case-control data. Simulations are conducted to evaluate the performance of our proposed approach, and a case-control study on genetic association with asthma is used to demonstrate the method.