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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, predictCox and iidCox:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.