An Introduction to G Methods

Robins' generalized methods (g methods) provide consistent estimates of contrasts (e.g. differences, ratios) of potential outcomes under a less restrictive set of identification conditions than do standard regression methods (e.g. linear, logistic, Cox regression). Uptake of g methods by epidem...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of epidemiology Vol. 46; no. 2; pp. dyw323 - 762
Main Authors Naimi, Ashley I, Cole, Stephen R, Kennedy, Edward H
Format Journal Article
LanguageEnglish
Published England Oxford University Press 01.04.2017
Subjects
Causality
Computer Simulation
Confounding Factors (Epidemiology)
Education Corner
Epidemiologic Research Design
Humans
Models, Statistical
G Methods
Marginal Structural Model
G Formula
Inverse Probability Weighting
Structural Nested Model
G Estimation
Monte Carlo Estimation
Online AccessGet full text
ISSN0300-5771
1464-3685
DOI10.1093/ije/dyw323

Cover

More Information
Summary:Robins' generalized methods (g methods) provide consistent estimates of contrasts (e.g. differences, ratios) of potential outcomes under a less restrictive set of identification conditions than do standard regression methods (e.g. linear, logistic, Cox regression). Uptake of g methods by epidemiologists has been hampered by limitations in understanding both conceptual and technical details. We present a simple worked example that illustrates basic concepts, while minimizing technical complications.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0300-5771
1464-3685
DOI:10.1093/ije/dyw323