Applied health scientists are increasingly dealing with complex data structures to answer questions about exposure effects and mediation. In such settings, feedback between confounders, exposures, and mediators render standard adjustment methods (regression, restriction, stratification, matching) inappropriate. The parametric g formula—one of three “g” methods—is a versatile tool that can be used to quantify a variety of exposure effects with complex data structures.
This workshop will provide a comprehensive overview of the g formula for identifying and estimating causal effects. After a brief introduction to the potential outcomes framework, we will review obstacles to effect estimation and mediation analysis with complex longitudinal data. The g formula will then be introduced with three examples using actual data and software code: (i) a simple simulated analysis that minimizes technical details and emphasizes core concepts; (ii) a mediation analysis setting where interest lies in direct/indirect effects; and (iii) a complex longitudinal data setting where interest lies in estimating the total effect of an exposure measured repeatedly over many months of follow-up. The goal of this workshop will be to enable participants to implement the parametric g formula in a range of settings, to articulate and evaluate key assumptions/limitations, and to implement critical model validation techniques. No prior knowledge of causal modeling, counterfactuals, or g methods is required.
Workshop Chair:
Ashley Naimi, University of Pittsburgh