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A simple unified approach for estimating natural direct and indirect effects

Theis Lange, Stijn Vansteelandt UGent and Maarten Bekaert UGent (2012) AMERICAN JOURNAL OF EPIDEMIOLOGY. 176(3). p.190-195
abstract
An important problem within both epidemiology and many social sciences is to break down the effect of a given treatment into different causal pathways and to quantify the importance of each pathway. Formal mediation analysis based on counterfactuals is a key tool when addressing this problem. During the last decade, the theoretical framework for mediation analysis has been greatly extended to enable the use of arbitrary statistical models for outcome and mediator. However, the researcher attempting to use these techniques in practice will often find implementation a daunting task, as it tends to require special statistical programming. In this paper, the authors introduce a simple procedure based on marginal structural models that directly parameterize the natural direct and indirect effects of interest. It tends to produce more parsimonious results than current techniques, greatly simplifies testing for the presence of a direct or an indirect effect, and has the advantage that it can be conducted in standard software. However, its simplicity comes at the price of relying on correct specification of models for the distribution of mediator (and exposure) and accepting some loss of precision compared with more complex methods. Web Appendixes 1 and 2, which are posted on the Journals Web site (http://aje.oupjournals.org/), contain implementation examples in SAS software (SAS Institute, Inc., Cary, North Carolina) and R language (R Foundation for Statistical Computing, Vienna, Austria).
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
MARGINAL STRUCTURAL MODELS, CAUSAL MEDIATION ANALYSIS, mediation, causal inference, marginal structural models, SENSITIVITY-ANALYSIS, INFERENCE, BIAS
journal title
AMERICAN JOURNAL OF EPIDEMIOLOGY
Am. J. Epidemiol.
volume
176
issue
3
pages
190 - 195
Web of Science type
Article
Web of Science id
000306923800003
JCR category
PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
JCR impact factor
4.78 (2012)
JCR rank
9/157 (2012)
JCR quartile
1 (2012)
ISSN
0002-9262
DOI
10.1093/aje/kwr525
language
English
UGent publication?
no
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1960901
handle
http://hdl.handle.net/1854/LU-1960901
date created
2011-12-06 14:26:25
date last changed
2015-06-17 09:52:43
@article{1960901,
  abstract     = {An important problem within both epidemiology and many social sciences is to break down the effect of a given treatment into different causal pathways and to quantify the importance of each pathway. Formal mediation analysis based on counterfactuals is a key tool when addressing this problem. During the last decade, the theoretical framework for mediation analysis has been greatly extended to enable the use of arbitrary statistical models for outcome and mediator. However, the researcher attempting to use these techniques in practice will often find implementation a daunting task, as it tends to require special statistical programming. In this paper, the authors introduce a simple procedure based on marginal structural models that directly parameterize the natural direct and indirect effects of interest. It tends to produce more parsimonious results than current techniques, greatly simplifies testing for the presence of a direct or an indirect effect, and has the advantage that it can be conducted in standard software. However, its simplicity comes at the price of relying on correct specification of models for the distribution of mediator (and exposure) and accepting some loss of precision compared with more complex methods. Web Appendixes 1 and 2, which are posted on the Journals Web site (http://aje.oupjournals.org/), contain implementation examples in SAS software (SAS Institute, Inc., Cary, North Carolina) and R language (R Foundation for Statistical Computing, Vienna, Austria).},
  author       = {Lange, Theis and Vansteelandt, Stijn and Bekaert, Maarten},
  issn         = {0002-9262},
  journal      = {AMERICAN JOURNAL OF EPIDEMIOLOGY},
  keyword      = {MARGINAL STRUCTURAL MODELS,CAUSAL MEDIATION ANALYSIS,mediation,causal inference,marginal structural models,SENSITIVITY-ANALYSIS,INFERENCE,BIAS},
  language     = {eng},
  number       = {3},
  pages        = {190--195},
  title        = {A simple unified approach for estimating natural direct and indirect effects},
  url          = {http://dx.doi.org/10.1093/aje/kwr525},
  volume       = {176},
  year         = {2012},
}

Chicago
Lange, Theis, Stijn Vansteelandt, and Maarten Bekaert. 2012. “A Simple Unified Approach for Estimating Natural Direct and Indirect Effects.” American Journal of Epidemiology 176 (3): 190–195.
APA
Lange, T., Vansteelandt, S., & Bekaert, M. (2012). A simple unified approach for estimating natural direct and indirect effects. AMERICAN JOURNAL OF EPIDEMIOLOGY, 176(3), 190–195.
Vancouver
1.
Lange T, Vansteelandt S, Bekaert M. A simple unified approach for estimating natural direct and indirect effects. AMERICAN JOURNAL OF EPIDEMIOLOGY. 2012;176(3):190–5.
MLA
Lange, Theis, Stijn Vansteelandt, and Maarten Bekaert. “A Simple Unified Approach for Estimating Natural Direct and Indirect Effects.” AMERICAN JOURNAL OF EPIDEMIOLOGY 176.3 (2012): 190–195. Print.