Evidence map›Paper›PMID 35633840›Full record

ArticleJournal of causal inference2022

Decomposition of the total effect for two mediators: A natural mediated interaction effect framework.

Xin Gao, Li Li, Li Luo

Abstract read
In one paragraph

Article in Journal of causal inference, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Applied causal inference methods for sequential mediators.BMC medical research methodology · 2022
    Article
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

3 authors.

Xin GaoDepartment of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, 87131, USA; Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, 87131, USA.
Li LiDepartment of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, 87131, USA.
Li LuoComprehensive Cancer Center, University of New Mexico, Albuquerque, NM, 87131, USA; Department of Internal Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.

Funding

WOMEN'S CANCERS RESEARCH PROGRAMP30CA118100 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Yolanda Sanchez · 2005 to 2026
$57.1M
UNM Metals Exposure and Toxicity Assessment on tribal Lands in the Southwest (METALS) Superfund Research ProgramP42ES025589 · NIEHS · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI CERRATO, JOSE MANUEL · 2017 to 2025
$16.4M
Role of open dumping and open burning of solid waste in the generation of microplastics and products of incomplete combustion on tribal landsP50MD015706 · NIMHD · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI HOOVER, JOSEPH HAMILTON, LEWIS, JOHNNYE L · 2020 to 2024
$6.8M
NCI NIH HHS P30 CA118100NIEHS NIH HHS P42 ES025589NIMHD NIH HHS P50 MD015706
6 · The paper itself

Abstract

Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total effect (TE) of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade. In this work, we develop decompositions for scenarios where two mediators are causally sequential or non-sequential. Current developments in this area have primarily focused on either decompositions without interaction components or with interactions but assuming no causally sequential order between the mediators. We propose a new concept called natural mediated interaction (MI) effect that captures the two-way and three-way interactions for both scenarios and extends the two-way MIs in the literature. We develop a unified approach for decomposing the TE into the effects that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Finally, we compare our proposed decomposition to an existing method in a non-sequential two-mediator scenario using simulated data, and illustrate the proposed decomposition for a sequential two-mediator scenario using a real data analysis.

Indexed as

62P10causal inferencecausally sequential mediatorsinteractionmediation

Identifiers

PMID35633840
PMCPMC9139468

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.