Implementation of G-Computation on a Simulated Data Set: Demonstration of a Causal Inference Technique

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 approache...

Full description

Saved in:
Bibliographic Details
Published in:American journal of epidemiology 2011-04, Vol.173 (7), p.731-738
Main Authors: SNOWDEN, Jonathan M, ROSE, Sherri, MORTIMER, Kathleen M
Format: Article
Language:English
Subjects:
Biological and medical sciences
Causality
Chronic obstructive pulmonary disease, asthma
Computer Simulation
Confounding Factors, Epidemiologic
Environment. Living conditions
Epidemiologic Research Design
Epidemiology
General aspects
Humans
Medical sciences
Miscellaneous
Models, Statistical
Pneumology
Practice of Epidemiology
Public health. Hygiene
Public health. Hygiene-occupational medicine
Regression Analysis
Statistical inference
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0002-9262
1476-6256
DOI:10.1093/aje/kwq472