title: Inference for additive interaction under exposure misclassification.
abstract: Results are given concerning inferences that can be drawn about interaction when binary exposures are subject to certain forms of independent nondifferential misclassification. Tests for interaction, using the misclassified exposures, are valid provided the probability of misclassification satisfies certain bounds. Results are given for additive statistical interactions, for causal interactions corresponding to synergism in the sufficient cause framework and for so-called compositional epistasis. Both two-way and three-way interactions are considered. The results require only that the probability of misclassification be no larger than 1/2 or 1/4, depending on the test. For additive statistical interaction, a method to correct estimates and confidence intervals for misclassification is described. The consequences for power of interaction tests under exposure misclassification are explored through simulations.
authors.affiliation: Departments of Biostatistics and Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A. tvanderw@hsph.harvard.edu.
authors.forename: Tyler J
authors.initials: TJ
authors.lastname: Vanderweele
keywords: Causal inference; Epistasis; Interaction; Misclassification; Sufficient cause; Synergism