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

TL;DR

gfoRmula is an R package that implements the parametric g-formula to estimate causal effects of complex, time-varying treatment interventions in longitudinal studies, accommodating various outcome types and intervention strategies.

Contribution

The package provides an accessible implementation of the parametric g-formula for diverse treatment and outcome types, facilitating causal inference in complex longitudinal data.

Findings

Enables estimation of causal effects with time-varying treatments.

Supports binary, continuous, and survival outcomes.

Handles static, dynamic, and competing event interventions.

Abstract

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