gfoRmula

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.

Last 30 days

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.