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Flexible mediation analysis with multiple mediators

JOHAN STEEN, Tom Loeys UGent, Beatrijs Moerkerke and Stijn Vansteelandt UGent (2017) American Journal of Epidemiology. 186(2). p.184-193
abstract
The advent of counterfactual-based mediation analysis has triggered enormous progress on how, and under what assumptions, one may disentangle path-specific effects upon combining arbitrary (possibly nonlinear) models for mediator and outcome. However, current developments have largely focused on single mediators because required identification assumptions prohibit simple extensions to settings with multiple mediators that may depend on one another. In this article, we propose a procedure for obtaining fine-grained decompositions that may still be recovered from observed data in such complex settings. We first show that existing analytical approaches target specific instances of a more general set of decompositions and may therefore fail to provide a comprehensive assessment of the processes that underpin cause-effect relationships between exposure and outcome. We then outline conditions for obtaining the remaining set of decompositions. Because the number of targeted decompositions increases rapidly with the number of mediators, we introduce natural effects models along with estimation methods that allow for flexible and parsimonious modeling. Our procedure can easily be implemented using off-the-shelf software and is illustrated using a reanalysis of the World Health Organization's Large Analysis and Review of European Housing and Health Status (WHO-LARES) study on the effect of mold exposure on mental health (2002–2003).
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
causal inference, mediation analysis, flexible modeling, multiple mediators
journal title
American Journal of Epidemiology
volume
186
issue
2
pages
184 - 193
publisher
Oxford University Press (OUP)
ISSN
0002-9262
1476-6256
DOI
10.1093/aje/kwx051
language
English
UGent publication?
yes
classification
U
copyright statement
I don't know the status of the copyright for this publication
id
8519767
handle
http://hdl.handle.net/1854/LU-8519767
alternative location
https://academic.oup.com/aje/article-lookup/doi/10.1093/aje/kwx051
date created
2017-05-08 07:41:25
date last changed
2017-07-18 10:00:58
@article{8519767,
  abstract     = {The advent of counterfactual-based mediation analysis has triggered enormous progress on how, and under what assumptions, one may disentangle path-specific effects upon combining arbitrary (possibly nonlinear) models for mediator and outcome. However, current developments have largely focused on single mediators because required identification assumptions prohibit simple extensions to settings with multiple mediators that may depend on one another. In this article, we propose a procedure for obtaining fine-grained decompositions that may still be recovered from observed data in such complex settings. We first show that existing analytical approaches target specific instances of a more general set of decompositions and may therefore fail to provide a comprehensive assessment of the processes that underpin cause-effect relationships between exposure and outcome. We then outline conditions for obtaining the remaining set of decompositions. Because the number of targeted decompositions increases rapidly with the number of mediators, we introduce natural effects models along with estimation methods that allow for flexible and parsimonious modeling. Our procedure can easily be implemented using off-the-shelf software and is illustrated using a reanalysis of the World Health Organization's Large Analysis and Review of European Housing and Health Status (WHO-LARES) study on the effect of mold exposure on mental health (2002--2003).},
  author       = {STEEN, JOHAN and Loeys, Tom and Moerkerke, Beatrijs and Vansteelandt, Stijn},
  issn         = {0002-9262},
  journal      = {American Journal of Epidemiology},
  keyword      = {causal inference,mediation analysis,flexible modeling,multiple mediators},
  language     = {eng},
  number       = {2},
  pages        = {184--193},
  publisher    = {Oxford University Press (OUP)},
  title        = {Flexible mediation analysis with multiple mediators},
  url          = {http://dx.doi.org/10.1093/aje/kwx051},
  volume       = {186},
  year         = {2017},
}

Chicago
Steen, Johan, Tom Loeys, Beatrijs Moerkerke, and Stijn Vansteelandt. 2017. “Flexible Mediation Analysis with Multiple Mediators.” American Journal of Epidemiology 186 (2): 184–193.
APA
Steen, Johan, Loeys, T., Moerkerke, B., & Vansteelandt, S. (2017). Flexible mediation analysis with multiple mediators. American Journal of Epidemiology, 186(2), 184–193.
Vancouver
1.
Steen J, Loeys T, Moerkerke B, Vansteelandt S. Flexible mediation analysis with multiple mediators. American Journal of Epidemiology. Oxford University Press (OUP); 2017;186(2):184–93.
MLA
Steen, Johan, Tom Loeys, Beatrijs Moerkerke, et al. “Flexible Mediation Analysis with Multiple Mediators.” American Journal of Epidemiology 186.2 (2017): 184–193. Print.