title: An introduction to g methods.
abstract: 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.
authors.affiliation: Department of Epidemiology, University of Pittsburgh.
authors.forename: Ashley I
authors.initials: AI
authors.lastname: Naimi
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill and.
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Cole
authors.affiliation: Department of Statistics, Carnegie Mellon University.
authors.forename: Edward H
authors.initials: EH
authors.lastname: Kennedy
mesh_terms: D015984:Causality; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D015340:Epidemiologic Research Design; D006801:Humans; D015233:Models, Statistical
keywords: G Estimation; G Formula; G Methods; Inverse Probability Weighting; Marginal Structural Model; Monte Carlo Estimation; Structural Nested Model