Literature DB >> 21036954

Invited commentary: pushing the mediation envelope.

Thomas Ten Have1.   

Abstract

The very insightful and clear paper by VanderWeele and Vansteelandt in this issue of the Journal (Am J Epidemiol. 2010;172(12):1339-1348) bridges the gap between biostatistics methodologists focusing on causal methods for mediation analyses and the practitioners of mediational analyses to the benefit of both groups. In an effort to continue the bridging of this gap, this invited commentary relates the important issue of "natural direct effects" to the well-known epidemiologic method of direct standardization. Additionally, attention is paid to the importance of temporal sequencing to help substantiate the mediation relations among the exposure, mediation, and outcome. A crucial mathematical distortion under the logistics model, called "absence of collapsibility," is noted in motivating VanderWeele and Vansteelandt's use of the log-linear model for comparing the effect of exposure adjusted for the mediator with the effect of exposure unadjusted for the mediator. It is also noted that this issue applies to one approach to assessing confounding. Finally, some issues are raised for consideration when testing the interaction between the exposure and mediator before assessing mediation.

Mesh:

Year:  2010        PMID: 21036954      PMCID: PMC3105276          DOI: 10.1093/aje/kwq328

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  5 in total

Review 1.  Mediators and moderators of treatment effects in randomized clinical trials.

Authors:  Helena Chmura Kraemer; G Terence Wilson; Christopher G Fairburn; W Stewart Agras
Journal:  Arch Gen Psychiatry       Date:  2002-10

2.  Statistical assessment of mediational effects for logistic mediational models.

Authors:  Bin Huang; Siva Sivaganesan; Paul Succop; Elizabeth Goodman
Journal:  Stat Med       Date:  2004-09-15       Impact factor: 2.373

3.  On quantifying the magnitude of confounding.

Authors:  Holly Janes; Francesca Dominici; Scott Zeger
Journal:  Biostatistics       Date:  2010-03-04       Impact factor: 5.899

4.  Models for longitudinal data: a generalized estimating equation approach.

Authors:  S L Zeger; K Y Liang; P S Albert
Journal:  Biometrics       Date:  1988-12       Impact factor: 2.571

5.  Odds ratios for mediation analysis for a dichotomous outcome.

Authors:  Tyler J Vanderweele; Stijn Vansteelandt
Journal:  Am J Epidemiol       Date:  2010-10-29       Impact factor: 5.363

  5 in total

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