causalCmprsk - Nonparametric and Cox-based Estimation of ATE in Competing Risks

The causalCmprsk package is designed for estimation of average treatment effects (ATE) of two static treatment regimes on time-to-event outcomes with K competing events (K can be 1). The method uses propensity scores weighting for emulation of baseline randomization. The package accompanies the paper of Charpignon, Vakulenko-Lagun, Zheng, Magdamo et al., Drug repurposing of metformin for Alzheimer’s disease: Combining causal inference in medical record data and systems pharmacology for biomarker identification (submitted).

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.

The causalCmprsk package can be installed by


The examples of how to use causalCmprsk package are here.