Quantile Regression in the Secondary Analysis of Case-Control Data

• 成果类型: Article
• 署名作者: Wei, Ying; Song, Xiaoyu; Liu, Mengling; Ionita-Laza, Iuliana; Reibman, Joan
• 署名单位: Columbia University; Columbia University; New York University; New York University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1008101
• 发表日期: 2016
• 页码: 344-354
• 关键词: selection

摘要

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 article, 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. Supplementary materials for this article are available online.