Implements the parametric g-formula algorithm of Robins (1986) <doi:10.1016/0270-0255(86)90088-6>. The g-formula can be used to estimate the causal effects of hypothetical time-varying treatment interventions on the mean or risk of an outcome from longitudinal data with time-varying confounding. This package allows: 1) binary or continuous/multi-level time-varying treatments; 2) different types of outcomes (survival or continuous/binary end of follow-up); 3) data with competing events or truncation by death and loss to follow-up 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.
|Author||Victoria Lin [aut] (V. Lin and S. McGrath made equal contributions), Sean McGrath [aut, cre] (<https://orcid.org/0000-0002-7281-3516>, V. Lin and S. McGrath made equal contributions), Zilu Zhang [aut], Roger W. Logan [aut], Lucia C. Petito [aut], Jessica G. Young [aut] (<https://orcid.org/0000-0002-2758-6932>, 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 <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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