Implements the parametric gformula algorithm of Robins (1986) <doi:10.1016/02700255(86)900886>. The gformula can be used to estimate the causal effects of hypothetical timevarying treatment interventions on the mean or risk of an outcome from longitudinal data with timevarying confounding. This package allows: 1) binary or continuous/multilevel timevarying treatments; 2) different types of outcomes (survival or continuous/binary end of followup); 3) data with competing events or truncation by death and loss to followup and other types of censoring events; 4) different options for handling competing events in the case of survival outcomes; 5) a random measurement/visit process; 6) joint interventions on multiple treatments; and 7) general incorporation of a priori knowledge of the data structure.
Package details 


Author  Victoria Lin [aut] (V. Lin and S. McGrath made equal contributions), Sean McGrath [aut, cre] (<https://orcid.org/0000000272813516>, V. Lin and S. McGrath made equal contributions), Zilu Zhang [aut], Roger W. Logan [aut], Lucia C. Petito [aut], Jing Li [aut], Jessica G. Young [aut] (<https://orcid.org/0000000227586932>, M.A. Hernán and J.G. Young made equal contributions), Miguel A. Hernán [aut] (M.A. Hernán and J.G. Young made equal contributions), 2019 The President and Fellows of Harvard College [cph] 
Maintainer  Sean McGrath <sean_mcgrath@g.harvard.edu> 
License  GPL3 
Version  1.0.4 
URL  https://github.com/CausalInference/gfoRmula https://doi.org/10.1016/j.patter.2020.100008 
Package repository  View on CRAN 
Installation 
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