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.

Keywords: G Estimation; G Formula; G Methods; Inverse Probability Weighting; Marginal Structural Model; Monte Carlo Estimation; Structural Nested Model.

Figure 1.

Causal diagram representing the relation between anti-retroviral treatment at time 0 (A₀), HIV viral load just prior to the second round of treatment (Z₁), anti-retroviral treatment status at time 1 (A₁), the CD4 count measured at the end of follow-up (Y), and an unmeasured common cause (U) of HIV viral load and CD4.