ArticlePloS one2013
Assessing causal mechanistic interactions: a peril ratio index of synergy based on multiplicativity.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- A self-consistent probabilistic formulation for inference of interactions.Scientific reports · 2020Article
- The effect of body mass index and its interaction with family history on hypertension: a case-control study.Clinical hypertension · 2019Article
- Testing for Sufficient-Cause Interactions in Case-Control Studies of Non-Rare Diseases.Scientific reports · 2018Article
- Attributing diseases to multiple pathways: a causal-pie modeling approach.Clinical epidemiology · 2018Article
- Invited Commentary: The Continuing Need for the Sufficient Cause Model Today.American journal of epidemiology · 2017Article
- Sharp bounds on sufficient-cause interactions under the assumption of no redundancy.BMC medical research methodology · 2017Article
- Complementary Log Regression for Sufficient-Cause Modeling of Epidemiologic Data.Scientific reports · 2016Article
- Interactive effect of serum uric acid and total bilirubin for cardiovascular disease in Chinese patients with type 2 diabetes.Scientific reports · 2016Article
- Observational
- Article
- Article
- Estimation of a common effect parameter from follow-up data when there is no mechanistic interaction.PloS one · 2014Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
The assessments of interactions in epidemiology have traditionally been based on risk-ratio, odds-ratio or rate-ratio multiplicativity. However, many epidemiologists fail to recognize that this is mainly for statistical conveniences and often will misinterpret a statistically significant interaction as a genuine mechanistic interaction. The author adopts an alternative metric system for risk, the 'peril'. A peril is an exponentiated cumulative rate, or simply, the inverse of a survival (risk complement) or one plus an odds. The author proposes a new index based on multiplicativity of peril ratios, the 'peril ratio index of synergy based on multiplicativity' (PRISM). Under the assumption of no redundancy, PRISM can be used to assess synergisms in sufficient cause sense, i.e., causal co-actions or causal mechanistic interactions. It has a less stringent threshold to detect a synergy as compared to a previous index of 'relative excess risk due to interaction'. Using the new PRISM criterion, many situations in which there is not evidence of interaction judged by the traditional indices are in fact corresponding to bona fide positive or negative synergisms.
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