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Estimating Direct Effects in Cohort and Case–Control Studies
Abstract
Estimating the effect of an exposure on an outcome, other than through some given mediator, requires adjustment for all risk factors of the mediator that are also associated with the outcome. When these risk factors are themselves affected by the exposure, then standard regression methods do not apply. In this article, I review methods for accommodating this and discuss their limitations for estimating the controlled direct effect (ie, the exposure effect when controlling the mediator at a specified level uniformly in the population). In addition, I propose a powerful and easy-to-apply alternative that uses G-estimation in structural nested models to address these limitations both for cohort and case-control studies.
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Metadata
- Authors
- Vansteelandt, S.
- Affiliations
- Ghent University (BE)
- Corresponding author
- Vansteelandt, Stijn
- ORCID
- Vansteelandt, Stijn
- Venue
- Epidemiology
- Published
- 2009-11
- Volume
- 20
- Issue
- 6
- Pages
- 851-860
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W1971464691
- Cited by
- 159
- References
- 22
- Citations by year
- 2025: 6 · 2024: 9 · 2023: 11 · 2022: 11 · 2021: 9 · 2020: 11 · 2019: 8 · 2018: 8 · 2017: 7 · 2016: 15 · 2015: 7 · 2014: 12 · 2013: 8 · 2012: 17
- FWCI
- 10.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- controlled direct effect, G-estimation, cohort studies, case-control study
- MeSH terms
- Humans, Risk Factors, Models, Statistical, Cohort Studies, Epidemiologic Research Design, Causality, Confounding Factors, Epidemiologic, Outcome Assessment, Health Care
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