swMATH ID: 
19175

Software Authors: 
Thomas Alexander Gerds, Thomas Harder Scheike, Paul Blanche, Brice Ozenne

Description: 
R package riskRegression. Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks. Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the FineGray regression model and an interface for the combination of causespecific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly timedependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Crossvalidation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits. 
Homepage: 
https://cran.rproject.org/web/packages/riskRegression/index.html

Source Code: 
https://github.com/cran/riskRegression

Dependencies: 
R 
Keywords: 
CRAN;
R package;
Risk Regression Models;
Prediction Scores;
Survival Analysis;
Competing Risks

Related Software: 
R;
timereg;
cmprsk;
sandwich;
survival;
broom;
pseudo;
SAS;
geepack;
prodlim;
fastpseudo;
Cprob;
eventglm;
crrSC;
crrp;
doParallel;
crrstep;
fastcmprsk;
Compeir;
survsim

Cited in: 
1 Publication
