Evidence map›Paper›PMID 29696178›Full record

ArticleContemporary clinical trials communications2017

Comparison of methods for the analysis of relatively simple mediation models.

Judith J M Rijnhart, Jos W R Twisk, Mai J M Chinapaw, Michiel R de Boer, Martijn W Heymans

Abstract read
In one paragraph

Article in Contemporary clinical trials communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed.

  1. Trial
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  6. Urban green spaces and mental health: Findings from Uganda.Hygiene and environmental health advances · 2025
    Article
  7. Article
  8. Practical challenges in mediation analysis: a guide for applied researchers.Health services & outcomes research methodology · 2025
    Article
  9. Article
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  14. Sex Differences in Out-of-Hospital Cardiac Arrest Survival Trends.Journal of the American Heart Association · 2024
    Article
  15. Article
  16. Article
  17. Article
  18. Article
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  20. Assessing the Robustness of Mediation Analysis Results Using Multiverse Analysis.Prevention science : the official journal of the Society for Prevention Research · 2022
    Review
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

5 authors.

Judith J M RijnhartDepartment of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands.
Jos W R TwiskDepartment of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands.
Mai J M ChinapawDepartment of Public and Occupational Health, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands.
Michiel R de BoerDepartment of Methodology and Applied Biostatistics, Faculty of Earth and Life Sciences, Institute for Health Sciences, Amsterdam Public Health Research Institute, VU University, Amsterdam, The Netherlands.
Martijn W HeymansDepartment of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimsStatistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction.

methodsSecondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework.

resultsOLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction.

conclusionsSince OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them.

Indexed as

BMI, body mass indexCI, confidence intervalCross-sectional dataFIML, full-information maximum likelihoodIndirect effectMediation analysisOLS, ordinary least squareOrdinary least square regressionPotential outcomes frameworkSBC, sweetened beverages consumptionSEM, structural equation modelingSE, standard errorStructural equation modeling

Identifiers

PMID29696178
PMCPMC5898549

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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.