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

Ying Wei, Xiaoyu Song, Mengling Liu, Iuliana Ionita-Laza and Joan Reibman

Journal of the American Statistical Association, 2016, vol. 111, issue 513, 344-354

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.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2015.1008101 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:111:y:2016:i:513:p:344-354

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2015.1008101

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().