Evidence map›Paper›PMID 25063043›Full record

ArticlePersonality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc2015

Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis.

David P MacKinnon, Angela G Pirlott

Abstract read
In one paragraph

Article in Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing 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.

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

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

  1. A Systematic Review of the Reporting Quality of Observational Studies That Use Mediation Analyses.Prevention science : the official journal of the Society for Prevention Research · 2022
    Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Trial
  8. Trial
  9. Article
  10. Article
  11. Indirect Effects of the Coping Power Intervention on Latent Suicidal Thoughts and Behaviors: an Integrative Data Analysis.Prevention science : the official journal of the Society for Prevention Research · 2026
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Cost burden following esophagectomy: A single centre observational study.World journal of gastrointestinal surgery · 2024
    Article
  20. Article

22 more citing papers are in PubMed but not listed here.

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.

David P MacKinnonArizona State University, Tempe, USA David.MacKinnon@asu.edu.
Angela G PirlottUniversity of Wisconsin-Eau Claire, Eau Claire, USA.

Funding

Estimating Mediation Effects in Prevention StudiesR37DA009757 · NIDA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI MACKINNON, DAVID P · 2015 to 2024
$3.5M
ESTIMATING MEDIATION EFFECTS IN PREVENTION STUDIESR01DA009757 · NIDA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI MACKINNON, DAVID P · 1996 to 2013
$2.6M
NIDA NIH HHS DA09757NIDA NIH HHS R01 DA009757NIDA NIH HHS R37 DA009757
6 · The paper itself

Abstract

Statistical mediation methods provide valuable information about underlying mediating psychological processes, but the ability to infer that the mediator variable causes the outcome variable is more complex than widely known. Researchers have recently emphasized how violating assumptions about confounder bias severely limits causal inference of the mediator to dependent variable relation. Our article describes and addresses these limitations by drawing on new statistical developments in causal mediation analysis. We first review the assumptions underlying causal inference and discuss three ways to examine the effects of confounder bias when assumptions are violated. We then describe four approaches to address the influence of confounding variables and enhance causal inference, including comprehensive structural equation models, instrumental variable methods, principal stratification, and inverse probability weighting. Our goal is to further the adoption of statistical methods to enhance causal inference in mediation studies.

Indexed as

CausalityConfounding Factors, EpidemiologicStatistics as TopicHumanscausal inferenceconfounder biasmediation

Identifiers

PMID25063043
PMCPMC4835342

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.