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Researchers are often interested in using longitudinal data to estimate thecausal effects of hypothetical time-varying treatment interventions on the meanor risk of a future outcome. Standard regression/conditioning methods forconfounding control generally fail to recover causal effects when time-varyingconfounders are themselves affected by past treatment. In such settings,estimators derived from Robins's g-formula may recover time-varying treatmenteffects provided sufficient covariates are measured to control confounding byunmeasured risk factors. The package gfoRmula implements in R one suchestimator: the parametric g-formula. This estimator easily adapts to binary orcontinuous time-varying treatments as well as contrasts defined by static ordynamic, deterministic or random treatment interventions, as well asinterventions that depend on the natural value of treatment. The packageaccommodates survival outcomes as well as binary or continuous end of follow-upoutcomes. For survival outcomes, the package has different options for handlingcompeting events. This paper describes the gfoRmula package, along withmotivating background, features, and examples.

gfoRmula: An R package for estimating effects of general time-varying treatment interventions via the parametric g-formula