In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R inter face for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical functionals. The software presented here is implemented in the riskRegression package.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2017-062.zip
survival, rms, riskRegression, mstate, rbenchmark, profvis, mets
Survival, Econometrics, SocialSciences, ClinicalTrials, ReproducibleResearch
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For attribution, please cite this work as
Ozenne, et al., "The R Journal: riskRegression: Predicting the Risk of an Event using Cox Regression Models", The R Journal, 2017
BibTeX citation
@article{RJ-2017-062,
author = {Ozenne, Brice and Sørensen, Anne Lyngholm and Scheike, Thomas and Torp-Pedersen, Christian and Gerds, Thomas Alexander},
title = {The R Journal: riskRegression: Predicting the Risk of an Event using Cox Regression Models},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-062},
doi = {10.32614/RJ-2017-062},
volume = {9},
issue = {2},
issn = {2073-4859},
pages = {440-460}
}