Evidence map›Paper›PMID 26537707›Full record

ArticleEuropean journal of epidemiology2015

G-computation demonstration in causal mediation analysis.

Aolin Wang, Onyebuchi A Arah

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing 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

47 citing papers in PubMed.

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  13. Human Plasma Proteomics Links Modifiable Lifestyle Exposome to Disease Risk.medRxiv : the preprint server for health sciences · 2025
    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.

Aolin WangDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), 650 Charles E. Young Drive South, Los Angeles, CA, 90095-1772, USA. aolinw@ucla.edu.
Onyebuchi A ArahDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), 650 Charles E. Young Drive South, Los Angeles, CA, 90095-1772, USA.

Funding

Statistics and Methods CoreR24HD041022 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COSTA, DORA L · 2001 to 2016
$10.1M
The impact of natural experiments on child obesity: A systems science approachR01HD072296 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PRELIP, MICHAEL LEE, WANG, MAY C · 2013 to 2017
$2.4M
NICHD NIH HHS R01 HD072296NICHD NIH HHS R01-HD072296-01A1NICHD NIH HHS R24 HD041022NICHD NIH HHS R24-HD041022
6 · The paper itself

Abstract

Recent work has considerably advanced the definition, identification and estimation of controlled direct, and natural direct and indirect effects in causal mediation analysis. Despite the various estimation methods and statistical routines being developed, a unified approach for effect estimation under different effect decomposition scenarios is still needed for epidemiologic research. G-computation offers such unification and has been used for total effect and joint controlled direct effect estimation settings, involving different types of exposure and outcome variables. In this study, we demonstrate the utility of parametric g-computation in estimating various components of the total effect, including (1) natural direct and indirect effects, (2) standard and stochastic controlled direct effects, and (3) reference and mediated interaction effects, using Monte Carlo simulations in standard statistical software. For each study subject, we estimated their nested potential outcomes corresponding to the (mediated) effects of an intervention on the exposure wherein the mediator was allowed to attain the value it would have under a possible counterfactual exposure intervention, under a pre-specified distribution of the mediator independent of any causes, or under a fixed controlled value. A final regression of the potential outcome on the exposure intervention variable was used to compute point estimates and bootstrap was used to obtain confidence intervals. Through contrasting different potential outcomes, this analytical framework provides an intuitive way of estimating effects under the recently introduced 3- and 4-way effect decomposition. This framework can be extended to complex multivariable and longitudinal mediation settings.

Indexed as

CausalityModels, StatisticalMonte Carlo MethodBiasComputer SimulationConfounding Factors, EpidemiologicData Interpretation, StatisticalEffect Modifier, EpidemiologicEpidemiologic StudiesHumansRegression AnalysisRoot Cause AnalysisSmokingEffect decompositionG-computationMediation analysisParametric g-formulaSimulation

Identifiers

PMID26537707
PMCPMC4674449

What OpenQuestion holds

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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.