title: Bounds on causal interactions for binary outcomes.
abstract: A common goal of epidemiologic research is to study how two exposures interact in causing a binary outcome. Causal interaction is defined as the presence of subjects for which the causal effect of one exposure depends on the level of the other exposure. For binary exposures, it has previously been shown that the presence of causal interaction is testable through additive statistical interaction. However, it has also been shown that the magnitude of causal interaction, defined as the proportion of subjects for which there is causal interaction, is generally not identifiable. In this article, we derive bounds on causal interactions, which are applicable to binary outcomes and categorical exposures with arbitrarily many levels. These bounds can be used to assess the magnitude of causal interaction, and serve as an important complement to the statistical test that is frequently employed. The bounds are derived both without and with an assumption about monotone exposure effects. We present an application of the bounds to a study of gene-gene interaction in rheumatoid arthritis.
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: A
authors.initials: A
authors.lastname: Sjölander
authors.affiliation: Department of Statistics, Inha University, Incheon, Korea.
authors.forename: W
authors.initials: W
authors.lastname: Lee
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
authors.forename: H
authors.initials: H
authors.lastname: Källberg
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Y
authors.initials: Y
authors.lastname: Pawitan
mesh_terms: D017677:Age Distribution; D001172:Arthritis, Rheumatoid / Q000453:epidemiology* / Q000235:genetics*; D015984:Causality; D003198:Computer Simulation; D005260:Female; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics; D059811:HLA-DRB1 Chains / Q000235:genetics*; D006801:Humans; D016015:Logistic Models; D008297:Male; D017063:Outcome Assessment, Health Care / Q000379:methods*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D015995:Prevalence; D054596:Protein Tyrosine Phosphatase, Non-Receptor Type 22 / Q000235:genetics*; D015203:Reproducibility of Results; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D017678:Sex Distribution; D013548:Sweden / Q000453:epidemiology
chemical_list: D005819:Genetic Markers; D059811:HLA-DRB1 Chains; C516765:PTPN22 protein, human; D054596:Protein Tyrosine Phosphatase, Non-Receptor Type 22
keywords: Biologic interaction; Bounds; Causal interaction; Gene–gene interaction; Potential outcomes