EconPapers    
Economics at your fingertips  
 

Case-Control Inference of Interaction between Genetic and Nongenetic Risk Factors under Assumptions on Their Distribution

Shin Ji-Hyung, McNeney Brad and Graham Jinko
Additional contact information
Shin Ji-Hyung: Simon Fraser University
McNeney Brad: Simon Fraser University
Graham Jinko: Simon Fraser University

Statistical Applications in Genetics and Molecular Biology, 2007, vol. 6, issue 1, 1-43

Abstract: In genetic association studies, there is increasing interest in understanding the joint effects of genetic and nongenetic factors. For rare diseases, the case-control study is a practical design, and logistic regression is the standard method of inference. However, the power to detect statistical interaction is a concern, even with relatively large samples. Under independence of genetic and nongenetic covariates, improved precision of interaction estimators is possible, but logistic regression does not make use of this assumption and consequently is not statistically efficient. In recent work to improve efficiency, profile likelihood methods have been used to develop semi-parametric inference that incorporates the independence assumption. We describe an alternate derivation of these estimators for rare diseases that is based on classic arguments from case-control inference. These arguments lead to a simplification in the variance estimator. We also describe a strategy for relaxing the independence assumption. Under either independence or the proposed dependence model, inference for association parameters is conveniently obtained by fitting a conditional logistic regression. The statistical properties of the proposed methodology are investigated by simulation.

Date: 2007
References: View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1270 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:sagmbi:v:6:y:2007:i:1:n:13

Ordering information: This journal article can be ordered from
https://www.degruyter.com/view/j/sagmb

DOI: 10.2202/1544-6115.1270

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

 
Page updated 2021-05-07
Handle: RePEc:bpj:sagmbi:v:6:y:2007:i:1:n:13