Package: causalCmprsk 2.0.0

Bella Vakulenko-Lagun

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>.

Authors:Bella Vakulenko-Lagun [aut, cre], Colin Magdamo [aut], Marie-Laure Charpignon [aut], Bang Zheng [aut], Mark Albers [aut], Sudeshna Das [aut]

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Peer review:

Bug tracker:https://github.com/bella2001/causalcmprsk/issues

On CRAN:

5 exports 3 stars 1.11 score 16 dependencies 8 scripts 310 downloads

Last updated 1 years agofrom:af47e89026. Checks:OK: 7. Indexed: yes.

Exports:fit.coxfit.nonparget.numAtRiskget.pointEstget.weights

Dependencies:clicodetoolsdata.tabledoParallelforeachglueinlineiteratorslatticelifecyclemagrittrMatrixpurrrrlangsurvivalvctrs