Evidence map›Paper›PMID 22905648›Full record

ArticlePsychological methods2012

Assessing mediation using marginal structural models in the presence of confounding and moderation.

Donna L Coffman, Wei Zhong

Abstract read
In one paragraph

Article in Psychological methods, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Review
  4. Article
  5. Article
  6. Practical challenges in mediation analysis: a guide for applied researchers.Health services & outcomes research methodology · 2025
    Article
  7. Tutorial on causal mediation analysis with binary variables: An application to health psychology research.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2023
    Article
  8. Article
  9. Article
  10. Observational
  11. Article
  12. A biologist's guide to model selection and causal inference.Proceedings. Biological sciences · 2021
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
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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

2 authors.

Donna L CoffmanThe Methodology Center, Pennsylvania State University, State College, PA 16801, USA. dlc30@psu.edu
Wei Zhong

Funding

THE REFINEMENT &APPLICATION OF MEDICAL DECISION MAKING IN PREVENTION SCIENCESP50DA010075 · NIDA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI LANZA, STEPHANIE T · 1996 to 2014
$24.5M
Marginal Causal Models for Observational Studies in Obesity ResearchR21DK082858 · NIDDK · PENNSYLVANIA STATE UNIVERSITY, THE · PI COFFMAN, DONNA · 2009 to 2010
$321k
Causal Inference for Mediation Models in Substance Abuse Prevention ResearchR03DA026543 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI COFFMAN, DONNA LYNN · 2009 to 2010
$125k
NIDA NIH HHS P50 DA010075NIDA NIH HHS P50 DA100075-15NIDA NIH HHS R03 DA026543NIDA NIH HHS R03- DA026543-01NIDDK NIH HHS 521 DK082858-2NIDDK NIH HHS R21 DK082858
6 · The paper itself

Abstract

This article presents marginal structural models with inverse propensity weighting (IPW) for assessing mediation. Generally, individuals are not randomly assigned to levels of the mediator. Therefore, confounders of the mediator and outcome may exist that limit causal inferences, a goal of mediation analysis. Either regression adjustment or IPW can be used to take confounding into account, but IPW has several advantages. Regression adjustment of even one confounder of the mediator and outcome that has been influenced by treatment results in biased estimates of the direct effect (i.e., the effect of treatment on the outcome that does not go through the mediator). One advantage of IPW is that it can properly adjust for this type of confounding, assuming there are no unmeasured confounders. Further, we illustrate that IPW estimation provides unbiased estimates of all effects when there is a baseline moderator variable that interacts with the treatment, when there is a baseline moderator variable that interacts with the mediator, and when the treatment interacts with the mediator. IPW estimation also provides unbiased estimates of all effects in the presence of nonrandomized treatments. In addition, for testing mediation we propose a test of the null hypothesis of no mediation. Finally, we illustrate this approach with an empirical data set in which the mediator is continuous, as is often the case in psychological research.

Indexed as

Confounding Factors, EpidemiologicEffect Modifier, EpidemiologicHumansModels, StatisticalPropensity ScoreRegression Analysis

Identifiers

PMID22905648
PMCPMC3553264

What OpenQuestion holds

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Registered trials

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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.