Literature DB >> 26596350

Causal mediation analysis with multiple causally non-ordered mediators.

Masataka Taguri1,2, John Featherstone2, Jing Cheng2.   

Abstract

In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries.

Entities:  

Keywords:  Causal inference; effect decomposition; mediation analysis; multiple mediators; natural direct effect; natural indirect effect

Mesh:

Year:  2015        PMID: 26596350      PMCID: PMC5698181          DOI: 10.1177/0962280215615899

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  27 in total

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7.  Generalized causal mediation analysis.

Authors:  Jeffrey M Albert; Suchitra Nelson
Journal:  Biometrics       Date:  2011-02-09       Impact factor: 2.571

8.  Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis.

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9.  A unification of mediation and interaction: a 4-way decomposition.

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10.  Causal mediation analysis with multiple mediators.

Authors:  R M Daniel; B L De Stavola; S N Cousens; S Vansteelandt
Journal:  Biometrics       Date:  2014-10-28       Impact factor: 2.571

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  15 in total

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2.  FWER and FDR control when testing multiple mediators.

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Journal:  Bioinformatics       Date:  2018-07-15       Impact factor: 6.937

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5.  Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies.

Authors:  Yanyi Song; Xiang Zhou; Min Zhang; Wei Zhao; Yongmei Liu; Sharon L R Kardia; Ana V Diez Roux; Belinda L Needham; Jennifer A Smith; Bhramar Mukherjee
Journal:  Biometrics       Date:  2019-12-19       Impact factor: 2.571

6.  Sparse Principal Component based High-Dimensional Mediation Analysis.

Authors:  Yi Zhao; Martin A Lindquist; Brian S Caffo
Journal:  Comput Stat Data Anal       Date:  2019-09-03       Impact factor: 1.681

7.  Generalized causal mediation and path analysis: Extensions and practical considerations.

Authors:  Jeffrey M Albert; Jang Ik Cho; Yiying Liu; Suchitra Nelson
Journal:  Stat Methods Med Res       Date:  2018-06-05       Impact factor: 3.021

8.  BAYESIAN METHODS FOR MULTIPLE MEDIATORS: RELATING PRINCIPAL STRATIFICATION AND CAUSAL MEDIATION IN THE ANALYSIS OF POWER PLANT EMISSION CONTROLS.

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9.  Assessing Potential Outcomes Mediation in HIV Interventions.

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Journal:  AIDS Behav       Date:  2021-03-19

Review 10.  Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges.

Authors:  Ping Zeng; Zhonghe Shao; Xiang Zhou
Journal:  Comput Struct Biotechnol J       Date:  2021-05-26       Impact factor: 7.271

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