riskRegression: Predicting the Risk of an Event using Cox Regression Models

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.

Brice Ozenne , Anne Lyngholm Sørensen , Thomas Scheike , Christian Torp-Pedersen , Thomas Alexander Gerds
2017-11-22

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2017-062.zip

CRAN packages used

survival, rms, riskRegression, mstate, rbenchmark, profvis, mets

CRAN Task Views implied by cited packages

Survival, Econometrics, SocialSciences, ClinicalTrials, ReproducibleResearch

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Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Ozenne, et al., "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 = {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}
}