The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Bias Formulas for Sensitivity Analysis for Direct and Indirect Effects
Open accessAbstract
A key question in many studies is how to divide the total effect of an exposure into a component that acts directly on the outcome and a component that acts indirectly, ie, through some intermediate. For example, one might be interested in the extent to which the effect of diet on blood pressure is mediated through sodium intake and the extent to which it operates through other pathways. In the context of such mediation analysis, even if the effect of the exposure on the outcome is unconfounded, estimates of direct and indirect effects will be biased if control is not made for confounders of the mediator-outcome relationship. Often data are not collected on such mediator-outcome confounding variables; the results in this paper allow researchers to assess the sensitivity of their estimates of direct and indirect effects to the biases from such confounding. Specifically, the paper provides formulas for the bias in estimates of direct and indirect effects due to confounding of the exposure-mediator relationship and of the mediator-outcome relationship. Under some simplifying assumptions, the formulas are particularly easy to use in sensitivity analysis. The bias formulas are illustrated by examples in the literature concerning direct and indirect effects in which mediator-outcome confounding may be present.
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Citations
Cites
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Imai, K., Keele, L., & Tingley, D. (2010) · Psychological Methods · doi:10.1037/a0020761 · Cited by 4,143
A structural approach to selection bias
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VanderWeele, T. J., & Vansteelandt, S. (2009) · Statistics and Its Interface · doi:10.4310/sii.2009.v2.n4.a7 · Cited by 758
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Research works printed but not linked: 3
- Pearl, 2001, Proceedings of the Seventeenth Conference on Uncertainty and Artificial Intelligence., 411
- Hafeman, 2008, Opening the Black Box: A Reassessment of Mediation From a Counterfactual Perspective [dissertation].
- Avin, 2005, Proceedings of the International Joint Conferences on Artificial Intelligence., 357
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A general approach to causal mediation analysis.
Imai, K., Keele, L., & Tingley, D. (2010) · Psychological Methods · doi:10.1037/a0020761 · Cited by 4,143
Mediation Analysis: A Practitioner's Guide
VanderWeele, T. J. (2016) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-032315-021402 · Cited by 1,985
Self-citationValeri, L., & VanderWeele, T. J. (2013) · Psychological Methods · doi:10.1037/a0031034 · Cited by 2,132
“If there are confounders of the exposure–outcome, mediator–outcome, or exposure–mediator relationship then, to the extent possible, these should be controlled for in the models; otherwise sensitivity analysis techniques (VanderWeele, 2010; Imai, Keele, & Tingley, 2010) can be used.”
BackgroundMethodInfluentialSelf-citationIdentification, Inference and Sensitivity Analysis for Causal Mediation Effects
Imai, K., Keele, L., & Yamamoto, T. (2010) · Statistical Science · doi:10.1214/10-sts321 · Cited by 1,628
Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable
Elwert, F., & Winship, C. (2014) · Annual Review of Sociology · doi:10.1146/annurev-soc-071913-043455 · Cited by 1,168
Metadata
- Authors
- VanderWeele, T. J.
- Affiliations
- Harvard University (US)
- Corresponding author
- VanderWeele, Tyler J.
- Venue
- Epidemiology
- Published
- 2010-07
- Volume
- 21
- Issue
- 4
- Pages
- 540-551
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC4231822
- OpenAlex
- W2075031358
- Cited by
- 435
- References
- 42
- Citations by year
- 2026: 20 · 2025: 16 · 2024: 15 · 2023: 25 · 2022: 32 · 2021: 28 · 2020: 29 · 2019: 38 · 2018: 21 · 2017: 23 · 2016: 34 · 2015: 32 · 2014: 33 · 2013: 27 · 2012: 34
- FWCI
- 23.96
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Environmental Health Sciences (R01ES017876)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R03HD060696)
- 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
- direct and indirect effects, mediation analysis, sensitivity analysis, confounding, bias correction
- MeSH terms
- Brain, Cognition Disorders, Humans, Lead Poisoning, Organ Size, Regression Analysis, Risk Factors, Sensitivity and Specificity, Statistics as Topic, Models, Statistical, Bias
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