Nonparametric and Cox-Based Estimation of Average Treatment Effects in Competing Risks.
causalCmprsk - Nonparametric and Cox-based Estimation of Average Treatment Effects in Competing Risks
The causalCmprsk
package is designed for estimation of average treatment effects (ATE) of point interventions/treatments on time-to-event outcomes with K competing events (K can be 1). The method assumes that there is no unmeasured confounding and uses propensity scores weighting for emulation of baseline randomization.
The causalCmprsk
package provides two main functions: fit.cox
which assumes the Cox proportional hazards regression for potential outcomes, and fit.nonpar
that does not make any modeling assumptions for potential outcomes.
Installation
The causalCmprsk
package can be installed by
devtools::install_github("Bella2001/causalCmprsk")
Examples
The examples of how to use causalCmprsk
package on real data can be found here.
References
- M.-L. Charpignon, B. Vakulenko-Lagun, B. Zheng, C. Magdamo et al., Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia, 2022, Nature Communications.