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Quantile Regression in the Secondary Analysis of Case--Control Data

Author

Listed:
  • Ying Wei
  • Xiaoyu Song
  • Mengling Liu
  • Iuliana Ionita-Laza
  • Joan Reibman

Abstract

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.

Suggested Citation

  • Ying Wei & Xiaoyu Song & Mengling Liu & Iuliana Ionita-Laza & Joan Reibman, 2016. "Quantile Regression in the Secondary Analysis of Case--Control Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 344-354, March.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:513:p:344-354
    DOI: 10.1080/01621459.2015.1008101
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