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Invited commentary: G-Computation-lost in translation?

Stijn Vansteelandt UGent and Niels Keiding (2011) AMERICAN JOURNAL OF EPIDEMIOLOGY. 173(7). p.739-742
abstract
In this issue of the Journal, Snowden et al. (Am J Epidemiol. 2011;173(7):731-738) give a didactic explanation of G-computation as an approach for estimating the causal effect of a point exposure. The authors of the present commentary reinforce the idea that their use of G-computation is equivalent to a particular form of model-based standardization, whereby reference is made to the observed study population, a technique that epidemiologists have been applying for several decades. They comment on the use of standardized versus conditional effect measures and on the relative predominance of the inverse probability-of-treatment weighting approach as opposed to G-computation. They further propose a compromise approach, doubly robust standardization, that combines the benefits of both of these causal inference techniques and is not more difficult to implement.
Please use this url to cite or link to this publication:
author
organization
year
type
misc (editorialMaterial)
publication status
published
subject
keyword
CAUSAL INFERENCE, MARGINAL STRUCTURAL MODELS, MORTALITY, DISEASE, air pollution, asthma, regression analysis, simulation
in
AMERICAN JOURNAL OF EPIDEMIOLOGY
Am. J. Epidemiol.
volume
173
issue
7
pages
739 - 742
Web of Science type
Editorial Material
Web of Science id
000289301200003
JCR category
PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
JCR impact factor
5.216 (2011)
JCR rank
6/156 (2011)
JCR quartile
1 (2011)
ISSN
0002-9262
DOI
10.1093/aje/kwq474
language
English
UGent publication?
yes
classification
V
copyright statement
I have transferred the copyright for this publication to the publisher
id
1234526
handle
http://hdl.handle.net/1854/LU-1234526
date created
2011-05-24 16:19:20
date last changed
2016-12-19 15:47:43
@misc{1234526,
  abstract     = {In this issue of the Journal, Snowden et al. (Am J Epidemiol. 2011;173(7):731-738) give a didactic explanation of G-computation as an approach for estimating the causal effect of a point exposure. The authors of the present commentary reinforce the idea that their use of G-computation is equivalent to a particular form of model-based standardization, whereby reference is made to the observed study population, a technique that epidemiologists have been applying for several decades. They comment on the use of standardized versus conditional effect measures and on the relative predominance of the inverse probability-of-treatment weighting approach as opposed to G-computation. They further propose a compromise approach, doubly robust standardization, that combines the benefits of both of these causal inference techniques and is not more difficult to implement.},
  author       = {Vansteelandt, Stijn and Keiding, Niels},
  issn         = {0002-9262},
  keyword      = {CAUSAL INFERENCE,MARGINAL STRUCTURAL MODELS,MORTALITY,DISEASE,air pollution,asthma,regression analysis,simulation},
  language     = {eng},
  number       = {7},
  pages        = {739--742},
  series       = {AMERICAN JOURNAL OF EPIDEMIOLOGY},
  title        = {Invited commentary: G-Computation-lost in translation?},
  url          = {http://dx.doi.org/10.1093/aje/kwq474},
  volume       = {173},
  year         = {2011},
}

Chicago
Vansteelandt, Stijn, and Niels Keiding. 2011. “Invited Commentary: G-Computation-lost in Translation?” American Journal of Epidemiology.
APA
Vansteelandt, S., & Keiding, N. (2011). Invited commentary: G-Computation-lost in translation? AMERICAN JOURNAL OF EPIDEMIOLOGY.
Vancouver
1.
Vansteelandt S, Keiding N. Invited commentary: G-Computation-lost in translation? AMERICAN JOURNAL OF EPIDEMIOLOGY. 2011. p. 739–42.
MLA
Vansteelandt, Stijn, and Niels Keiding. “Invited Commentary: G-Computation-lost in Translation?” AMERICAN JOURNAL OF EPIDEMIOLOGY 2011 : 739–742. Print.