Description
Parametric G-Formula.
Description
Implements the non-iterative conditional expectation (NICE) algorithm of the g-formula algorithm (Robins (1986) <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). The g-formula can estimate an outcome's counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders. This package can be used for discrete or continuous time-varying treatments and for failure time outcomes or continuous/binary end of follow-up outcomes. The package can handle a random measurement/visit process and a priori knowledge of the data structure, as well as censoring (e.g., by loss to follow-up) and two options for handling competing events for failure time outcomes. Interventions can be flexibly specified, both as interventions on a single treatment or as joint interventions on multiple treatments. See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on how to use the package.
README.md
gfoRmula: Parametric G-Formula
Installation
You can install the released version of gfoRmula from CRAN with:
install.packages("gfoRmula")
After installing the devtools package (i.e., calling install.packages(devtools)), the development version of gfoRmula can be installed from GitHub with:
devtools::install_github("CausalInference/gfoRmula")
Usage
Please refer to McGrath et al. (2020) for a detailed guide to the gfoRmula package. Also, see the following vignettes regarding updates since McGrath et al. (2020):
- “A Simplified Approach for Specifying Interventions in gfoRmula”
- “Using Custom Outcome Models in gfoRmula”