ArticleAmerican journal of epidemiology2010
Marginal structural models for sufficient cause interactions.
Article in American journal of epidemiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Malaria, malnutrition, and birthweight: A meta-analysis using individual participant data.PLoS medicine · 2017Pooled it
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- Interaction between opium use and cigarette smoking on bladder cancer: An inverse probability weighting approach based on a multicenter case-control study in Iran.Global epidemiology · 2025Article
- Assessing Additive Interactions between Protective Factors Using Relative Risk Reduction Due to Interaction.Medicina (Kaunas, Lithuania) · 2024Article
- Causal Inference: Onward and Upward!Journal of dental research · 2022Article
- Association between maternal exposure to indoor air pollution and offspring congenital heart disease: a case-control study in East China.BMC public health · 2022Article
- Commonly Initiated Opioids and Risk of Fracture Hospitalizations in United States Nursing Homes.Drugs & aging · 2018Article
- Epidemiology of Moderate Alcohol Consumption and Breast Cancer: Association or Causation?Cancers · 2018Review
- Intergenerational Neighborhood Attainment and the Legacy of Racial Residential Segregation: A Causal Mediation Analysis.Demography · 2017Article
- Invited Commentary: The Continuing Need for the Sufficient Cause Model Today.American journal of epidemiology · 2017Article
- Disparities at the intersection of marginalized groups.Social psychiatry and psychiatric epidemiology · 2016Review
- Longitudinal Study of the Effects of Bacteremia and Sepsis on 5-year Risk of Cardiovascular Events.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2016Article
- 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
- The current deconstruction of paradoxes: one sign of the ongoing methodological "revolution".European journal of epidemiology · 2015Article
- Formalizing the role of agent-based modeling in causal inference and epidemiology.American journal of epidemiology · 2015Article
- Bounds on sufficient-cause interaction.European journal of epidemiology · 2014Article
- Invited commentary: Some advantages of the relative excess risk due to interaction (RERI)--towards better estimators of additive interaction.American journal of epidemiology · 2014Article
- Causation and causal inference for genetic effects.Human genetics · 2012Review
- Invited commentary: assessing mechanistic interaction between coinfecting pathogens for diarrheal disease.American journal of epidemiology · 2012Article
- Semiparametric tests for sufficient cause interaction.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Sufficient cause interactions concern cases in which there is a particular causal mechanism for some outcome that requires the presence of 2 or more specific causes to operate. Empirical conditions have been derived to test for sufficient cause interactions. However, when regression outcome models are used to control for confounding variables in tests for sufficient cause interactions, the outcome models impose restrictions on the relation between the confounding variables and certain unidentified background causes within the sufficient cause framework; often, these assumptions are implausible. By using marginal structural models, rather than outcome regression models, to test for sufficient cause interactions, modeling assumptions are instead made on the relation between the causes of interest and the confounding variables; these assumptions will often be more plausible. The use of marginal structural models also allows for testing for sufficient cause interactions in the presence of time-dependent confounding. Such time-dependent confounding may arise in cases in which one factor of interest affects both the second factor of interest and the outcome. It is furthermore shown that marginal structural models can be used not only to test for sufficient cause interactions but also to give lower bounds on the prevalence of such sufficient cause interactions.
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