ArticleEuropean journal of epidemiology2014
Bounds on sufficient-cause interaction.
Article in European journal of epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Sharp bounds on sufficient-cause interactions under the assumption of no redundancy.BMC medical research methodology · 2017Article
- Caution: work in progress : While the methodological "revolution" deserves in-depth study, clinical researchers and senior epidemiologists should not be disenfranchised.European journal of epidemiology · 2016Article
- Mastering variation: variance components and personalised medicine.Statistics in medicine · 2016Article
- The Rotterdam Study: 2016 objectives and design update.European journal of epidemiology · 2015Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A common goal of epidemiologic research is to study how two exposures interact in causing a binary outcome. Sufficient-cause interaction is a special type of mechanistic interaction, which requires that two events (e.g. specific exposure levels from two risk factors) are necessary in order for the outcome to occur. Recently, tests have been derived to establish the presence of sufficient-cause interactions, for categorical exposures with at most three levels. In this paper we derive prevalence bounds, i.e. lower and upper bounds on the prevalence of subjects for which sufficient-cause interaction is present. The derived bounds hold for categorical exposures with arbitrary many levels. We apply the bounds to data from a study of gene-gene interaction in the development of Rheumatoid Arthritis. We provide an R-program to estimate the bounds from real data .
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