Literature DB >> 20502339

Alternative assumptions for the identification of direct and indirect effects.

Danella M Hafeman1, Tyler J VanderWeele.   

Abstract

The assessment of mediation is important for testing the mechanisms that explain an observed relationship between exposure and disease. Several types of direct and indirect effects have been defined, broadly characterized as either controlled or natural. The identification of these effects requires a stricter set of assumptions than those necessary for the identification of the total effect of exposure on disease. The particular assumptions that are required differ depending on the type of effect. We use an approach based on response types to derive new assumptions for the identification of direct and indirect effects, both controlled and natural. These assumptions are stated in terms of response types and potential outcomes, and are compared with those already in the literature. This approach yields an alternative, and sometimes less stringent, set of assumptions for the identification of direct and indirect effects than those previously proposed.

Mesh:

Year:  2011        PMID: 20502339     DOI: 10.1097/EDE.0b013e3181c311b2

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  26 in total

1.  Targeted maximum likelihood estimation of natural direct effects.

Authors:  Wenjing Zheng; Mark J van der Laan
Journal:  Int J Biostat       Date:  2012-01-06       Impact factor: 0.968

2.  Attributable fractions for sufficient cause interactions.

Authors:  Tyler J VanderWeele
Journal:  Int J Biostat       Date:  2010-02-22       Impact factor: 0.968

3.  Bias formulas for sensitivity analysis for direct and indirect effects.

Authors:  Tyler J VanderWeele
Journal:  Epidemiology       Date:  2010-07       Impact factor: 4.822

4.  A complete graphical criterion for the adjustment formula in mediation analysis.

Authors:  Ilya Shpitser; Tyler J VanderWeele
Journal:  Int J Biostat       Date:  2011-03-04       Impact factor: 0.968

Review 5.  Identification of operating mediation and mechanism in the sufficient-component cause framework.

Authors:  Etsuji Suzuki; Eiji Yamamoto; Toshihide Tsuda
Journal:  Eur J Epidemiol       Date:  2011-03-30       Impact factor: 8.082

6.  Direct and indirect effects for neighborhood-based clustered and longitudinal data.

Authors:  T J VanderWeele
Journal:  Sociol Methods Res       Date:  2010-05-01

7.  Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting.

Authors:  Quynh C Nguyen; Theresa L Osypuk; Nicole M Schmidt; M Maria Glymour; Eric J Tchetgen Tchetgen
Journal:  Am J Epidemiol       Date:  2015-02-17       Impact factor: 4.897

8.  Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders.

Authors:  Tyler J VanderWeele; Yasutaka Chiba
Journal:  Epidemiol Biostat Public Health       Date:  2014

9.  Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data.

Authors:  Jing Huang; Ying Yuan; David Wetter
Journal:  Psychometrika       Date:  2019-01-03       Impact factor: 2.500

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

Authors:  Donna L Coffman; Wei Zhong
Journal:  Psychol Methods       Date:  2012-08-20
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