%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 }