Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros.

Valeri, Linda; VanderWeele, Tyler J.

doi:10.1037/a0031034

Cited by 2,132Advanced Causal Inference TechniquesStatistical Methods and Bayesian InferenceMeta-analysis and systematic reviews

Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros.

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Valeri, Linda, VanderWeele, Tyler J.·2013·Psychological Methods

DOI 10.1037/a0031034PMC PMC3659198

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Abstract

Mediation analysis is a useful and widely employed approach to studies in the field of psychology and in the social and biomedical sciences. The contributions of this article are several-fold. First we seek to bring the developments in mediation analysis for nonlinear models within the counterfactual framework to the psychology audience in an accessible format and compare the sorts of inferences about mediation that are possible in the presence of exposure-mediator interaction when using a counterfactual versus the standard statistical approach. Second, the work by VanderWeele and Vansteelandt (2009, 2010) is extended here to allow for dichotomous mediators and count outcomes. Third, we provide SAS and SPSS macros to implement all of these mediation analysis techniques automatically, and we compare the types of inferences about mediation that are allowed by a variety of software macros.

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We performed quantitative bias analyses for unmeasured confounding using an E-value approach for the mediation analyses.
“We performed quantitative bias analyses for unmeasured confounding using an E-value approach(36,37) for the mediation analyses.”
From Social inequalities in COVID-19 death by area-level income in 11.2 million people in Ontario, Canada: patterns over time and the mediating role of vaccination. (2026) · cites this paper as [36]
The Discussion (page unavailable) recommends sensitivity analysis when unmeasured confounding may affect mediation estimates. It does not describe an E-value approach or support that specific method for the citing authors’ quantitative bias analysis. (LLM, conf=0.87)
Experimental — accuracy not yet measured
E-values from 1.0-1.5, 1.5-3.0, and >3.0 suggest weak, moderate and strong unmeasured confounding, respectively.
“E-values from 1.0-1.5, 1.5-3.0, and >3.0 suggest weak, moderate and strong unmeasured confounding, respectively(36).”
From Social inequalities in COVID-19 death by area-level income in 11.2 million people in Ontario, Canada: patterns over time and the mediating role of vaccination. (2026) · cites this paper as [36]
The Discussion and Identification passages (pages not provided) discuss unmeasured confounding and recommend sensitivity analysis. A search for E-values and the stated numerical ranges found no passage defining those ranges or labeling them weak, moderate, and strong. (LLM, conf=0.76)
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“Consistent with recommendations for optimization trials, we did not power the study specifically to test for interactions among conditions [34, 37].”

Authors: Valeri, L., & VanderWeele, T. J.
Affiliations: Harvard University (US)
ORCID: Valeri, Linda
VanderWeele, Tyler J.
Venue: Psychological Methods
Publisher: American Psychological Association (APA)
Published: 2013-06
Volume: 18
Issue: 2
Pages: 137-150
ISSN: 1082-989X (ISSN-L)
1939-1463
DOI: 10.1037/a0031034
PMC: PMC3659198
OpenAlex: W2005441291
Cited by: 2,132
References: 14
Citations by year: 2026: 150 · 2025: 201 · 2024: 234 · 2023: 224 · 2022: 217 · 2021: 222 · 2020: 243 · 2019: 161 · 2018: 120 · 2017: 113 · 2016: 102 · 2015: 60 · 2014: 52 · 2013: 18 · 2012: 13
FWCI: 68.25
Type: journal article
Language: en
Update notices: correction (2014-01-13)
Open access: hybrid
doi.org
Licence: other-oa
www.apa.org/pubs/journals/resources/open-access.aspx (vor)
Locations: Psychological Methods (publishedVersion, other-oa)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health (ES017876; HD060696)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords: mediation analysis, counterfactual framework, nonlinear models, count outcomes, causal inference, SAS macro
MeSH terms: Data Interpretation, Statistical, Humans, Psychology, Regression Analysis, Research Design, Software, User-Computer Interface, Causality, Confounding Factors, Epidemiologic, Nonlinear Dynamics

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Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros.

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