# Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

## Documentation for package ‘riskRegression’ version 2020.12.08

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D F G I L M P R S T W

## -- A --

anova.ate | Risk Comparison Over Time
ARR | Risk Regression Fits a regression model for the risk of an event - allowing for competing risks.
as.data.table.ate | Turn ate Object Into a 'data.table'
as.data.table.influenceTest | Turn influenceTest Object Into a 'data.table'
as.data.table.predictCox | Turn predictCox Object Into a 'data.table'
as.data.table.predictCSC | Turn predictCSC Object Into a 'data.table'
ate | Average Treatment Effects Computation
autoplot.ate | Plot Average Risks
autoplot.predictCox | Plot Predictions From a Cox Model
autoplot.predictCSC | Plot Predictions From a Cause-specific Cox Proportional Hazard Regression
autoplot.Score | ggplot AUC curve

## -- B --

boot2pvalue | Compute the p.value from the distribution under H1
boxplot.Score | Boxplot risk quantiles

## -- C --

calcSeCox | Computation of standard errors for predictions
calcSeCSC | Standard error of the absolute risk predicted from cause-specific Cox models
Cforest | S3-wrapper function for cforest from the party package
coef.CauseSpecificCox | Extract coefficients from a Cause-Specific Cox regression model
coef.riskRegression | Extract coefficients from riskRegression model
colCenter_cpp | Apply - by column
colCumProd | Apply cumprod in each column
colCumSum | Apply cumsum in each column
colMultiply_cpp | Apply * by column
colScale_cpp | Apply / by column
colSumsCrossprod | Apply crossprod and colSums
confint.ate | Confidence Intervals and Confidence Bands for the Predicted Absolute Risk (Cumulative Incidence Function)
confint.influenceTest | Confidence Intervals and Confidence Bands for the Difference Between Two Estimates
confint.predictCox | Confidence Intervals and Confidence Bands for the predicted Survival/Cumulative Hazard
confint.predictCSC | Confidence Intervals and Confidence Bands for the Predicted Absolute Risk (Cumulative Incidence Function)
coxBaseEstimator | Extract the type of estimator for the baseline hazard
coxBaseEstimator.coxph | Extract the type of estimator for the baseline hazard
coxBaseEstimator.phreg | Extract the type of estimator for the baseline hazard
coxCenter | Extract the mean value of the covariates
coxCenter.coxph | Extract the mean value of the covariates
coxCenter.cph | Extract the mean value of the covariates
coxCenter.phreg | Extract the mean value of the covariates
coxFormula | Extract the formula from a Cox model
coxFormula.coxph | Extract the formula from a Cox model
coxFormula.cph | Extract the formula from a Cox model
coxFormula.glm | Extract the formula from a Cox model
coxFormula.phreg | Extract the formula from a Cox model
coxLP | Compute the linear predictor of a Cox model
coxLP.coxph | Compute the linear predictor of a Cox model
coxLP.cph | Compute the linear predictor of a Cox model
coxLP.phreg | Compute the linear predictor of a Cox model
coxModelFrame | Extract the design matrix used to train a Cox model
coxModelFrame.coxph | Extract the design matrix used to train a Cox model
coxModelFrame.cph | Extract the design matrix used to train a Cox model
coxModelFrame.phreg | Extract the design matrix used to train a Cox model
coxN | Extract the number of observations from a Cox model
coxN.CauseSpecificCox | Extract the number of observations from a Cox model
coxN.coxph | Extract the number of observations from a Cox model
coxN.cph | Extract the number of observations from a Cox model
coxN.glm | Extract the number of observations from a Cox model
coxN.phreg | Extract the number of observations from a Cox model
coxSpecial | Special characters in Cox model
coxSpecial.coxph | Special characters in Cox model
coxSpecial.cph | Special characters in Cox model
coxSpecial.phreg | Special characters in Cox model
coxStrata | Define the strata for a new dataset
coxStrata.coxph | Define the strata for a new dataset
coxStrata.cph | Define the strata for a new dataset
coxStrata.phreg | Define the strata for a new dataset
coxStrataLevel | Returns the name of the strata in Cox model
coxStrataLevel.coxph | Returns the name of the strata in Cox model
coxStrataLevel.cph | Returns the name of the strata in Cox model
coxStrataLevel.phreg | Returns the name of the strata in Cox model
coxVarCov | Extract the variance covariance matrix of the beta from a Cox model
coxVarCov.coxph | Extract the variance covariance matrix of the beta from a Cox model
coxVarCov.cph | Extract the variance covariance matrix of the beta from a Cox model
coxVarCov.phreg | Extract the variance covariance matrix of the beta from a Cox model
coxVariableName | Extract variable names from a model
CSC | Cause-specific Cox proportional hazard regression
Ctree | S3-Wrapper for ctree.

## -- D --

discreteRoot | Dichotomic search for monotone function

## -- F --

FGR | Formula wrapper for crr from cmprsk

## -- G --

getSplitMethod | Input for data splitting algorithms

## -- I --

iid.wglm | IID for IPCW Logistic Regressions
iidCox | Extract iid decomposition from a Cox model
iidCox.CauseSpecificCox | Extract iid decomposition from a Cox model
iidCox.coxph | Extract iid decomposition from a Cox model
iidCox.cph | Extract iid decomposition from a Cox model
iidCox.phreg | Extract iid decomposition from a Cox model
influenceTest | Influence test [Experimental!!]
influenceTest.default | Influence test [Experimental!!]
influenceTest.list | Influence test [Experimental!!]
information.wglm | Information for IPCW Logistic Regressions
IPA | Explained variation for settings with binary, survival and competing risk outcome
IPA.CauseSpecificCox | Explained variation for settings with binary, survival and competing risk outcome
IPA.coxph | Explained variation for settings with binary, survival and competing risk outcome
IPA.default | Explained variation for settings with binary, survival and competing risk outcome
IPA.glm | Explained variation for settings with binary, survival and competing risk outcome
ipcw | Estimation of censoring probabilities
ipcw.aalen | Estimation of censoring probabilities
ipcw.cox | Estimation of censoring probabilities
ipcw.marginal | Estimation of censoring probabilities
ipcw.none | Estimation of censoring probabilities
ipcw.nonpar | Estimation of censoring probabilities

## -- L --

LRR | Risk Regression Fits a regression model for the risk of an event - allowing for competing risks.

## -- M --

Melanoma | Malignant melanoma data
model.matrix.cph | Extract design matrix for cph objects
model.matrix.phreg | Extract design matrix for phreg objects

## -- P --

Paquid | Paquid sample
penalizedS3 | S3-wrapper for S4 function penalized
plot.riskRegression | Plotting predicted risk
plotAUC | Plot of time-dependent AUC curves
plotBrier | Plot Brier curve
plotCalibration | Plot Calibration curve
plotEffects | Plotting time-varying effects from a risk regression model.
plotPredictRisk | Plotting predicted risks curves.
plotRisk | plot predicted risks
plotROC | Plot ROC curves
predict.CauseSpecificCox | Predicting Absolute Risk from Cause-Specific Cox Models
predict.FGR | Predict subject specific risks (cumulative incidence) based on Fine-Gray regression model
predict.riskRegression | Predict individual risk.
predictBig.CauseSpecificCox | Predicting Absolute Risk from Cause-Specific Cox Models
predictCox | Fast computation of survival probabilities, hazards and cumulative hazards from Cox regression models
predictCoxPL | Computation of survival probabilities from Cox regression models using the product limit estimator.
predictRisk | Extrating predicting risks from regression models
predictRisk.aalen | Extrating predicting risks from regression models
predictRisk.ARR | Extrating predicting risks from regression models
predictRisk.BinaryTree | Extrating predicting risks from regression models
predictRisk.CauseSpecificCox | Extrating predicting risks from regression models
predictRisk.cox.aalen | Extrating predicting risks from regression models
predictRisk.coxph | Extrating predicting risks from regression models
predictRisk.coxph.penal | Extrating predicting risks from regression models
predictRisk.coxphTD | Extrating predicting risks from regression models
predictRisk.cph | Extrating predicting risks from regression models
predictRisk.CSCTD | Extrating predicting risks from regression models
predictRisk.default | Extrating predicting risks from regression models
predictRisk.double | Extrating predicting risks from regression models
predictRisk.factor | Extrating predicting risks from regression models
predictRisk.FGR | Extrating predicting risks from regression models
predictRisk.flexsurvreg | Extrating predicting risks from regression models
predictRisk.formula | Extrating predicting risks from regression models
predictRisk.gbm | Extrating predicting risks from regression models
predictRisk.glm | Extrating predicting risks from regression models
predictRisk.integer | Extrating predicting risks from regression models
predictRisk.lrm | Extrating predicting risks from regression models
predictRisk.matrix | Extrating predicting risks from regression models
predictRisk.numeric | Extrating predicting risks from regression models
predictRisk.pecCforest | Extrating predicting risks from regression models
predictRisk.pecCtree | Extrating predicting risks from regression models
predictRisk.penfitS3 | Extrating predicting risks from regression models
predictRisk.prodlim | Extrating predicting risks from regression models
predictRisk.psm | Extrating predicting risks from regression models
predictRisk.randomForest | Extrating predicting risks from regression models
predictRisk.ranger | Extrating predicting risks from regression models
predictRisk.rfsrc | Extrating predicting risks from regression models
predictRisk.riskRegression | Extrating predicting risks from regression models
predictRisk.rpart | Extrating predicting risks from regression models
predictRisk.selectCox | Extrating predicting risks from regression models
predictRisk.singleEventCB | Extrating predicting risks from regression models
predictRisk.SuperPredictor | Extrating predicting risks from regression models
predictRisk.survfit | Extrating predicting risks from regression models
predictRisk.wglm | Extrating predicting risks from regression models
print.ate | Print Average Treatment Effects
print.CauseSpecificCox | Print of a Cause-Specific Cox regression model
print.FGR | Print of a Fine-Gray regression model
print.influenceTest | Output of the DIfference Between Two Estimates
print.IPA | Print IPA object
print.predictCox | Print Predictions From a Cox Model
print.predictCSC | Print Predictions From a Cause-specific Cox Proportional Hazard Regression
print.riskRegression | Print function for riskRegression models
print.Score | Print Score object
print.subjectWeights | Print subject weights

## -- R --

reconstructData | Reconstruct the original dataset
riskLevelPlot | Level plots for risk prediction models
riskRegression | Risk Regression Fits a regression model for the risk of an event - allowing for competing risks.
riskRegression.options | Global options for 'riskRegression'
rowCenter_cpp | Apply - by row
rowCumProd | Apply cumprod in each row
rowCumSum | Apply cumsum in each row
rowMultiply_cpp | Apply * by row
rowPaste | Collapse Rows of Characters.
rowScale_cpp | Apply / by row
rowSumsCrossprod | Apply crossprod and rowSums
rsquared | Explained variation for settings with binary, survival and competing risk outcome
rsquared.CauseSpecificCox | Explained variation for settings with binary, survival and competing risk outcome
rsquared.coxph | Explained variation for settings with binary, survival and competing risk outcome
rsquared.default | Explained variation for settings with binary, survival and competing risk outcome
rsquared.glm | Explained variation for settings with binary, survival and competing risk outcome

## -- S --

sampleData | Simulate data with binary or time-to-event outcome
sampleDataTD | Simulate data with binary or time-to-event outcome
Score | Score risk predictions
Score.list | Score risk predictions
score.wglm | Score for IPCW Logistic Regressions
selectCox | Backward variable selection in the Cox regression model
selectJump | Evaluate the influence function at selected times
simActiveSurveillance | Simulate data of a hypothetical active surveillance prostate cancer study
simMelanoma | Simulate data alike the Melanoma data
sliceMultiplyPointer_cpp | Apply * by slice
sliceMultiply_cpp | Apply * by slice
sliceScalePointer_cpp | Apply / by slice
sliceScale_cpp | Apply / by slice
SmcFcs | SmcFcs
splitStrataVar | Reconstruct each of the strata variables
subjectWeights | Estimation of censoring probabilities at subject specific times
subjectWeights.aalen | Estimation of censoring probabilities at subject specific times
subjectWeights.cox | Estimation of censoring probabilities at subject specific times
subjectWeights.km | Estimation of censoring probabilities at subject specific times
subjectWeights.marginal | Estimation of censoring probabilities at subject specific times
subjectWeights.none | Estimation of censoring probabilities at subject specific times
subjectWeights.nonpar | Estimation of censoring probabilities at subject specific times
subsetIndex | Extract Specific Elements From An Object
subsetIndex.default | Extract Specific Elements From An Object
subsetIndex.matrix | Extract Specific Elements From An Object
summary.ate | Summary Average Treatment Effects
summary.FGR | Summary of a Fine-Gray regression model
summary.riskRegression | Summary of a risk regression model
summary.Score | Summary of prediction performance metrics
SuperPredictor | Formula interface for SuperLearner::SuperLearner
SurvResponseVar | Extract the time and event variable from a Cox model

## -- T --

terms.phreg | Extract terms for phreg objects
transformCI | Compute Confidence Intervals using a transformation
transformCIBP | Compute Confidence Intervals/Bands and P-values After a Transformation
transformIID | Compute Influence Functions after Transformation
transformSE | Compute Standard Errors after Transformation
transformT | Compute T-statistic after a Transformation

## -- W --

wglm | Logistic Regression Using IPCW