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The Semiparametric Case-Only Estimator

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We propose a semiparametric case-only estimator of multiplicative gene–environment or gene–gene interactions, under the assumption of conditional independence of the two factors given a vector of potential confounding variables. Our estimator yields valid inferences on the interaction function if either but not necessarily both of two unknown baseline functions of the confounders is correctly modeled. Furthermore, when both models are correct, our estimator has the smallest possible asymptotic variance for estimating the interaction parameter in a semiparametric model that assumes that at least one but not necessarily both baseline models are correct.
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Keywords: Double robustness; Generalized odds ratio; Gene–environment independence; Gene–environment interaction; Local efficiency

Document Type: Research Article

Affiliations: Departments of Epidemiology and Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, Massachusetts 02115, U.S.A.

Publication date: 01 December 2010

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