The R Journal: article published in 2017, volume 9:2

riskRegression: Predicting the Risk of an Event using Cox Regression Models PDF download
Brice Ozenne, Anne Lyngholm Sørensen, Thomas Scheike, Christian Torp-Pedersen and Thomas Alexander Gerds , The R Journal (2017) 9:2, pages 440-460.

Abstract 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.

Received: 2017-07-07; online 2017-11-22, supplementary material, (1.7 KiB)
CRAN packages: survival, rms, riskRegression, mstate, rbenchmark, profvis, mets
CRAN Task Views cited directly: Survival
CRAN Task Views implied by cited CRAN packages: Survival, Econometrics, SocialSciences, ClinicalTrials, ReproducibleResearch

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {Brice Ozenne and Anne Lyngholm Sørensen and Thomas Scheike
          and Christian Torp-Pedersen and Thomas Alexander Gerds},
  title = {{riskRegression: Predicting the Risk of an Event using Cox
          Regression Models}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-062},
  url = {},
  pages = {440--460},
  volume = {9},
  number = {2}