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 Fine-Gray regression model and an interface for the combination of cause-specific 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 time-dependent 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. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.

Documentation

Manual: riskRegression.pdf
Vignette: None available.

Maintainer: Thomas Alexander Gerds <tag at biostat.ku.dk>

Author(s): Thomas Alexander Gerds, Thomas Harder Scheike, Paul Blanche, Brice Ozenne

Install package and any missing dependencies by running this line in your R console:

install.packages("riskRegression")

Depends R (>= 2.9.0), data.table(>=1.10.4), ggplot2(>=2.1.0), prodlim(>=1.5.7)
Imports stats, graphics, survival(>=2.40.1), lava(>=1.4.7), cmprsk, doParallel, foreach, parallel, Rcpp, rms(>=5.0-0)
Suggests boot, CoxBoost, Daim, mets, party, pec, penalized, pROC, randomForest, randomForestSRC, rbenchmark, rpart, testthat, timereg
Enhances
Linking to Rcpp, RcppArmadillo
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Package riskRegression
Materials
URL
Task Views Survival
Version 1.3.7
Published 2017-03-10
License GPL (>= 2)
BugReports
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks riskRegression check results
Package source riskRegression_1.3.7.tar.gz