Multiply robust inference for statistical interactions.

ABSTRACT: A primary focus of an increasing number of scientific studies is to determine whether two exposures interact in the effect that they produce on an outcome of interest. Interaction is commonly assessed by fitting regression models in which the linear predictor includes the product between those exposures. When the main interest lies in the interaction, this approach is not entirely satisfactory because it is prone to (possibly severe) bias when the main exposure effects or the association between outcome and extraneous factors are misspecified. In this article, we therefore consider conditional mean models with identity or log link which postulate the statistical interaction in terms of a finite-dimensional parameter, but which are otherwise unspecified. We show that estimation of the inter

SUBMITTER: Vansteelandt S 

PROVIDER: S-EPMC3097121 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature