causalCmprsk
Nonparametric and Cox-Based Estimation of Average Treatment Effects in Competing Risks
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.
- Version2.0.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release07/04/2023
Documentation
Team
Bella Vakulenko-Lagun
Colin Magdamo
Show author detailsRolesAuthorMarie-Laure Charpignon
Show author detailsRolesAuthorBang Zheng
Show author detailsRolesAuthorMark Albers
Show author detailsRolesAuthorSudeshna Das
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 273 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 4 times.
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Last 365 days
This package has been downloaded 3,757 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 24, 2024 with 33 downloads.
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Dependencies
- Imports6 packages
- Suggests13 packages