Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks.
Install
library(devtools)
install_github("tagteam/riskRegression")
References
The following references provide the methodological framework for the features of riskRegression.
T.A. Gerds and M.W. Kattan (2021). Medical Risk Prediction Models: With Ties to Machine Learning (1st ed.) Chapman and Hall/CRC https://doi.org/10.1201/9781138384484
T.A. Gerds and M. Schumacher. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical Journal, 48(6):1029--1040, 2006.
T.A. Gerds and M. Schumacher. Efron-type measures of prediction error for survival analysis. Biometrics, 63(4):1283--1287, 2007.
T.A. Gerds, T. Cai, and M. Schumacher. The performance of risk prediction models. Biometrical Journal, 50(4):457--479, 2008.
U B Mogensen, H. Ishwaran, and T A Gerds. Evaluating random forests for survival analysis using prediction error curves. Journal of Statistical Software, 50(11), 2012.
P. Blanche, J-F Dartigues, and H. Jacqmin-Gadda. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Statistics in Medicine, 32(30): 5381--5397, 2013.
Paul Blanche, Ce'cile Proust-Lima, Lucie Loube`re, Claudine Berr, Jean- Franc,ois Dartigues, and He'le`ne Jacqmin-Gadda. Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks. Biometrics, 71 (1):102--113, 2015.
Functions predict.CauseSpecificCox
{.verbatim}, predictCox
{.verbatim} and iidCox
{.verbatim}:
- Brice Ozenne, Anne Lyngholm Sorensen, Thomas Scheike, Christian Torp-Pedersen and Thomas Alexander Gerds. riskRegression: Predicting the Risk of an Event using Cox Regression Models. The R Journal (2017) 9:2, pages 440-460.
@article{gerds2006consistent,
title = {Consistent Estimation of the Expected {B}rier Score
in General Survival Models with Right-Censored Event
Times},
author = {Gerds, T.A. and Schumacher, M.},
journal = {Biometrical Journal},
volume = 48,
number = 6,
pages = {1029--1040},
year = 2006,
publisher = {Wiley Online Library}
}
@article{gerds2007efron,
title = {Efron-Type Measures of Prediction Error for Survival
Analysis},
author = {Gerds, T.A. and Schumacher, M.},
journal = {Biometrics},
volume = 63,
number = 4,
pages = {1283--1287},
year = 2007,
publisher = {Wiley Online Library}
}
@article{gerds2008performance,
title = {The performance of risk prediction models},
author = {Gerds, T.A. and Cai, T. and Schumacher, M.},
journal = {Biometrical Journal},
volume = 50,
number = 4,
pages = {457--479},
year = 2008,
publisher = {Wiley Online Library}
}
@Article{mogensen2012pec,
title = {Evaluating random forests for survival analysis
using prediction error curves},
author = {Mogensen, U B and Ishwaran, H. and Gerds, T A},
journal = {Journal of Statistical Software},
year = 2012,
volume = 50,
number = 11
}
@article{Blanche2013statmed,
title = "{Estimating and comparing time-dependent areas under
receiver operating characteristic curves for
censored event times with competing risks}",
author = {Blanche, P. and Dartigues, J-F and Jacqmin-Gadda,
H.},
journal = {Statistics in Medicine},
volume = 32,
number = 30,
pages = {5381--5397},
year = 2013
}
@article{blanche2015,
title = {Quantifying and comparing dynamic predictive
accuracy of joint models for longitudinal marker and
time-to-event in presence of censoring and competing
risks},
author = {Blanche, Paul and Proust-Lima, C{\'e}cile and
Loub{\`e}re, Lucie and Berr, Claudine and Dartigues,
Jean-Fran{\c{c}}ois and Jacqmin-Gadda,
H{\'e}l{\`e}ne},
journal = {Biometrics},
volume = 71,
number = 1,
pages = {102--113},
year = 2015,
publisher = {Wiley Online Library}
}
@article{ozenne2017,
title = {riskRegression: Predicting the Risk of an Event
using Cox Regression Modelss},
author = {Ozenne, Brice and Sørensen, Anne Lyngholm
and Scheike, Thomas and Torp-Pedersen, Christian
and Gerds, Thomas Alexander},
journal = {The R Journal},
volume = 9,
number = 2,
pages = {440--460},
year = 2017
}