Advanced search
1 file | 112.04 KB

A simple unified approach for estimating natural direct and indirect effects

(2012) AMERICAN JOURNAL OF EPIDEMIOLOGY. 176(3). p.190-195
Author
Organization
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).
Keywords
MARGINAL STRUCTURAL MODELS, CAUSAL MEDIATION ANALYSIS, mediation, causal inference, marginal structural models, SENSITIVITY-ANALYSIS, INFERENCE, BIAS

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 112.04 KB

Citation

Please use this url to cite or link to this publication:

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.
@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},
}

Altmetric
View in Altmetric
Web of Science
Times cited: