G-Computation to Estimate Interpretable Epidemiological Effects

Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting. It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) and has been described in detail by Ahern, Hubbard, and Galea (2009) ; Snowden, Rose, and Mortimer (2011) ; and Westreich et al. (2012) .


Reference manual

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0.1.0 by Jessica Grembi, a year ago

Browse source code at https://github.com/cran/riskCommunicator

Authors: Jessica Grembi [aut, cre, cph] , Elizabeth Rogawski McQuade [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports boot, dplyr, ggplot2, gridExtra, magrittr, purrr, stats, rlang, tidyr, tidyselect, tidyverse

Suggests knitr, rmarkdown, testthat, printr

See at CRAN