Journal article
New perspective on the benefits of the gene-environment independence in case-control studies
Canadian journal of statistics, v 47(3), pp 473-486
01 Sep 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We study the benefit of exploiting the gene-environment independence (GEI) assumption for inferring the joint effect of genotype and environmental exposure on disease risk in a case-control study. By transforming the problem into a constrained maximum likelihood estimation problem we derive the asymptotic distribution of the maximum likelihood estimator (MLE) under the GEI assumption (MLE-GEI) in a closed form. Our approach uncovers a transparent explanation of the efficiency gained by exploiting the GEI assumption in more general settings, thus bridging an important gap in the existing literature. Moreover, we propose an easy-to-implement numerical algorithm for estimating the model parameters in practice. Finally, we conduct simulation studies to compare the proposed method with the traditional prospective logistic regression method and the case-only estimator. The Canadian Journal of Statistics 47: 473-486; 2019 (c) 2019 Statistical Society of Canada
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Details
- Title
- New perspective on the benefits of the gene-environment independence in case-control studies
- Creators
- Hao Luo - University of British ColumbiaGabriela Cohen Freue - Univ British Columbia, Dept Stat, Vancouver, BC V6T 1Z4, CanadaXin Zhao - University of British ColumbiaAlexandre Bouchard-Cote - University of British ColumbiaIgor Burstyn - Drexel UniversityPaul Gustafson - University of British Columbia
- Publication Details
- Canadian journal of statistics, v 47(3), pp 473-486
- Publisher
- Wiley
- Number of pages
- 14
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Environmental and Occupational Health
- Web of Science ID
- WOS:000481512700009
- Scopus ID
- 2-s2.0-85067872195
- Other Identifier
- 991019168242204721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Statistics & Probability