causalCmprsk: Nonparametric and Cox-Based Estimation of Average Treatment Effects in Competing Risks (original) (raw)
Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>.
Version:
2.0.0
Depends:
R (≥ 4.0.0)
Imports:
survival, inline, doParallel, parallel, utils, foreach, data.table, purrr, methods
Suggests:
knitr, rmarkdown, bookdown, tidyverse, ggalt, cobalt, ggsci, modEvA, naniar, DT, Hmisc, hrbrthemes, summarytools
Published:
2023-07-04
DOI:
10.32614/CRAN.package.causalCmprsk
Author:
Bella Vakulenko-Lagun [aut, cre], Colin Magdamo [aut], Marie-Laure Charpignon [aut], Bang Zheng [aut], Mark Albers [aut], Sudeshna Das [aut]
Maintainer:
Bella Vakulenko-Lagun
BugReports:
https://github.com/Bella2001/causalCmprsk/issues
License:
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:
https://github.com/Bella2001/causalCmprsk
NeedsCompilation:
no
Materials:
CRAN checks: