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J Am Stat Assoc. 2016;111(513):344-354. doi: 10.1080/01621459.2015.1008101. Epub 2016 May 5.

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

Author information

1
Department of Biostatistics, Columbia University, New York, NY 10032.
2
Department of Population Health, New York University School of Medicine, New York, NY 10016.
3
Department of Medicine, New York University School of Medicine, New York, NY 10016.

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 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.

KEYWORDS:

GWAS; case-control studies; estimating equations; quantile regression; secondary phenotype

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