Package: gfoRmula 1.1.0

gfoRmula: Parametric G-Formula

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.

Authors:Victoria Lin [aut], Sean McGrath [aut, cre], Zilu Zhang [aut], Roger W. Logan [aut], Lucia C. Petito [aut], Jing Li [aut], McGee Emma [aut], Cheng Carrie [aut], Jessica G. Young [aut], Miguel A. Hernán [aut], 2019 The President and Fellows of Harvard College [cph]