Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.
Am J Epidemiol
; 187(2): 366-377, 2018 02 01.
Article
em En
| MEDLINE
| ID: mdl-28633381
ABSTRACT
There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Estudos de Casos e Controles
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Projetos de Pesquisa Epidemiológica
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Interação Gene-Ambiente
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article