Evidence map›Paper›PMID 34782926›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2023

Statistical Mediation Analysis for Models with a Binary Mediator and a Binary Outcome: the Differences Between Causal and Traditional Mediation Analysis.

Judith J M Rijnhart, Matthew J Valente, Heather L Smyth, David P MacKinnon

Registry-linked trialAbstract read
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06666647 (Ambulance and Helicopter Response Times in Danish Emergency Medical Services), which is not on this map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06666647 active not recruitingnot on this mapstarted 2025, after this paper: background citation

Ambulance and Helicopter Response Times in Danish Emergency Medical Services: Protocol for an Epidemiological Study. The AHRTEMIS Study

TypeobservationalSponsorPeter Martin HansenRan2025 to 2026Enrolled2,500,000ConditionsEmergency Medical Services, Response, Triage, Survival OutcomesArmsTreatment by ambulance or helicopter services with response times
3 · Its place in the literature

Who cites it

44 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  12. Common Methylenetetrahydrofolate Reductase PolymorphismDiagnostics (Basel, Switzerland) · 2025
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  16. Lower Hippocampal Volume Partly Mediates the Association Between rs6859 in themedRxiv : the preprint server for health sciences · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Judith J M RijnhartDepartment of Epidemiology and Data Science, Amsterdam UMC, Location VU University Medical Center, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands. j.rijnhart@amsterdamumc.nl.ORCID 0000-0002-1046-3741
Matthew J ValenteCenter for Children and Families, Department of Psychology, Florida International University, Miami, FL, USA.
Heather L SmythDepartment of Psychology, Arizona State University, Tempe, AZ, USA.
David P MacKinnonDepartment of Psychology, Arizona State University, Tempe, AZ, USA.

Funding

Estimating Mediation Effects in Prevention StudiesR37DA009757 · NIDA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI MACKINNON, DAVID P · 2015 to 2024
$3.5M
Modern Longitudinal Mediation Methods for Prevention StudiesF31DA043317 · NIDA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI VALENTE, MATTHEW JOHN · 2017 to 2018
$65k
NIDA NIH HHS F31 DA043317NIDA NIH HHS R37 DA009757
6 · The paper itself

Abstract

Mediation analysis is an important statistical method in prevention research, as it can be used to determine effective intervention components. Traditional mediation analysis defines direct and indirect effects in terms of linear regression coefficients. It is unclear how these traditional effects are estimated in settings with binary variables. An important recent methodological advancement in the mediation analysis literature is the development of the causal mediation analysis framework. Causal mediation analysis defines causal effects as the difference between two potential outcomes. These definitions can be applied to any mediation model to estimate natural direct and indirect effects, including models with binary variables and an exposure-mediator interaction. This paper aims to clarify the similarities and differences between the causal and traditional effect estimates for mediation models with a binary mediator and a binary outcome. Causal and traditional mediation analyses were applied to an empirical example to demonstrate these similarities and differences. Causal and traditional mediation analysis provided similar controlled direct effect estimates, but different estimates of the natural direct effects, natural indirect effects, and total effect. Traditional mediation analysis methods do not generalize well to mediation models with binary variables, while the natural effect definitions can be applied to any mediation model. Causal mediation analysis is therefore the preferred method for the analysis of mediation models with binary variables.

Indexed as

Mediation AnalysisResearch DesignCausalityHumansLinear ModelsModels, StatisticalBinary mediatorBinary outcomeCausal inferenceCounterfactualLogistic regressionMediation analysisPotential outcomes

Identifiers

PMID34782926
PMCPMC9108123

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.