The growing body of work in the epidemiology literature focused on G-computation includes theoretical explanations of the method but very few simulations or examples of application. The small number of G-computation analyses in the epidemiology literature relative to other causal inference approaches may be partially due to a lack of didactic explanations of the method targeted toward an epidemiology audience. The authors provide a step-by-step demonstration of G-computation that is intended to familiarize the reader with this procedure. The authors simulate a data set and then demonstrate both G-computation and traditional regression to draw connections and illustrate contrasts between their implementation and interpretation relative to the truth of the simulation protocol. A marginal structural model is used for effect estimation in the G-computation example. The authors conclude by answering a series of questions to emphasize the key characteristics of causal inference techniques and the G-computation procedure in particular.
%0 Journal Article
%1 Snowden2011
%A Snowden, Jonathan M
%A Rose, Sherri
%A Mortimer, Kathleen M
%D 2011
%J American journal of epidemiology
%K Causality ComputerSimulation ConfoundingFactors(Epidemiology) EpidemiologicResearchDesign Humans Models RegressionAnalysis Statistical
%N 7
%P 731-8
%R 10.1093/aje/kwq472
%T Implementation of G-computation on a simulated data set: demonstration of a causal inference technique.
%U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3105284&tool=pmcentrez&rendertype=abstract
%V 173
%X The growing body of work in the epidemiology literature focused on G-computation includes theoretical explanations of the method but very few simulations or examples of application. The small number of G-computation analyses in the epidemiology literature relative to other causal inference approaches may be partially due to a lack of didactic explanations of the method targeted toward an epidemiology audience. The authors provide a step-by-step demonstration of G-computation that is intended to familiarize the reader with this procedure. The authors simulate a data set and then demonstrate both G-computation and traditional regression to draw connections and illustrate contrasts between their implementation and interpretation relative to the truth of the simulation protocol. A marginal structural model is used for effect estimation in the G-computation example. The authors conclude by answering a series of questions to emphasize the key characteristics of causal inference techniques and the G-computation procedure in particular.
%@ 1476-6256; 0002-9262
@article{Snowden2011,
abstract = {The growing body of work in the epidemiology literature focused on G-computation includes theoretical explanations of the method but very few simulations or examples of application. The small number of G-computation analyses in the epidemiology literature relative to other causal inference approaches may be partially due to a lack of didactic explanations of the method targeted toward an epidemiology audience. The authors provide a step-by-step demonstration of G-computation that is intended to familiarize the reader with this procedure. The authors simulate a data set and then demonstrate both G-computation and traditional regression to draw connections and illustrate contrasts between their implementation and interpretation relative to the truth of the simulation protocol. A marginal structural model is used for effect estimation in the G-computation example. The authors conclude by answering a series of questions to emphasize the key characteristics of causal inference techniques and the G-computation procedure in particular.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Snowden, Jonathan M and Rose, Sherri and Mortimer, Kathleen M},
biburl = {https://www.bibsonomy.org/bibtex/23e915d0a22c0297d34a2da129dc18e4b/jepcastel},
doi = {10.1093/aje/kwq472},
interhash = {462ff8b439f3f69d92e46d7917b5e6b8},
intrahash = {3e915d0a22c0297d34a2da129dc18e4b},
isbn = {1476-6256; 0002-9262},
issn = {1476-6256},
journal = {American journal of epidemiology},
keywords = {Causality ComputerSimulation ConfoundingFactors(Epidemiology) EpidemiologicResearchDesign Humans Models RegressionAnalysis Statistical},
month = {4},
note = {6073<m:linebreak></m:linebreak>JID: 7910653; aheadofprint;},
number = 7,
pages = {731-8},
pmid = {21415029},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Implementation of G-computation on a simulated data set: demonstration of a causal inference technique.},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3105284&tool=pmcentrez&rendertype=abstract},
volume = 173,
year = 2011
}