ArticleAmerican journal of epidemiology2011
A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models.
Article in American journal of epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
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- Towards robust causal inference in epidemiologic research: employing double cross-fit TMLE in right heart catheterization data.American journal of epidemiology · 2025Article
- Simultaneously Dealing With Immortal Time Bias and Residual Confounding: A Case Study of a High-Dimensional Propensity Score Approach With a Nested Case-Control Framework in Multiple Sclerosis Research.Pharmacoepidemiology and drug safety · 2025Article
- 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
- A flexible matching strategy for matched nested case-control studies.Annals of epidemiology · 2023Article
- Interactive effects of maternal exposure to chemical fertilizer and socio-economic status on the risk of low birth weight.BMC public health · 2022Article
- Causal inference methods for vaccine sieve analysis with effect modification.Statistics in medicine · 2022Article
- Impact of PD-1 gene polymorphism and its interaction with tea drinking on susceptibility to tuberculosis.Epidemiology and infection · 2021Article
- Impact ofWorld journal of clinical cases · 2020Article
- Tobacco smoking, chewing habits, alcohol drinking and the risk of head and neck cancer in Nepal.International journal of cancer · 2020Article
- A comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants.International journal of cancer · 2019Article
- Testing for Sufficient-Cause Interactions in Case-Control Studies of Non-Rare Diseases.Scientific reports · 2018Article
- Invited Commentary: The Continuing Need for the Sufficient Cause Model Today.American journal of epidemiology · 2017Article
- Complementary Log Regression for Sufficient-Cause Modeling of Epidemiologic Data.Scientific reports · 2016Article
- Commentary: The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?International journal of epidemiology · 2016Article
- Standardized binomial models for risk or prevalence ratios and differences.International journal of epidemiology · 2015Article
- Early pregnancy maternal vitamin D concentrations and risk of gestational diabetes mellitus.Paediatric and perinatal epidemiology · 2015Review
- Impact of MBL and MASP-2 gene polymorphism and its interaction on susceptibility to tuberculosis.BMC infectious diseases · 2015Article
- Invited commentary: Some advantages of the relative excess risk due to interaction (RERI)--towards better estimators of additive interaction.American journal of epidemiology · 2014Article
- A double robust approach to causal effects in case-control studies.American journal of epidemiology · 2014Article
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Abstract
Estimates of additive interaction from case-control data are often obtained by logistic regression; such models can also be used to adjust for covariates. This approach to estimating additive interaction has come under some criticism because of possible misspecification of the logistic model: If the underlying model is linear, the logistic model will be misspecified. The authors propose an inverse probability of treatment weighting approach to causal effects and additive interaction in case-control studies. Under the assumption of no unmeasured confounding, the approach amounts to fitting a marginal structural linear odds model. The approach allows for the estimation of measures of additive interaction between dichotomous exposures, such as the relative excess risk due to interaction, using case-control data without having to rely on modeling assumptions for the outcome conditional on the exposures and covariates. Rather than using conditional models for the outcome, models are instead specified for the exposures conditional on the covariates. The approach is illustrated by assessing additive interaction between genetic and environmental factors using data from a case-control study.
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