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Install

library(devtools)
install_github("tagteam/riskRegression")

References

The following references provide the methodological framework for the features of riskRegression.

  1. 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

  2. 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.

  3. T.A. Gerds and M. Schumacher. Efron-type measures of prediction error for survival analysis. Biometrics, 63(4):1283--1287, 2007.

  4. T.A. Gerds, T. Cai, and M. Schumacher. The performance of risk prediction models. Biometrical Journal, 50(4):457--479, 2008.

  5. 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.

  6. 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.

  7. 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
}

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Version

Install

install.packages('riskRegression')

Monthly Downloads

8,755

Version

2022.03.22

License

GPL (>= 2)

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Last Published

March 23rd, 2022

Functions in riskRegression (2022.03.22)

IPA

Explained variation for settings with binary, survival and competing risk outcome
SuperPredictor

Formula interface for SuperLearner::SuperLearner
IC_Nelson_Aalen_cens_time

Influence function for Nelson-Aalen estimator.
as.data.table.influenceTest

Turn influenceTest Object Into a data.table
as.data.table.predictCSC

Turn predictCSC Object Into a data.table
autoplot.ate

Plot Average Risks
autoplot.Score

ggplot AUC curve
Ctree

S3-Wrapper for ctree.
autoplot.predictCSC

Plot Predictions From a Cause-specific Cox Proportional Hazard Regression
anova.ate

Risk Comparison Over Time
FGR

Formula wrapper for crr from cmprsk
as.data.table.ate

Turn ate Object Into a data.table
autoplot.predictCox

Plot Predictions From a Cox Model
colCenter_cpp

Apply - by column
coef.riskRegression

Extract coefficients from riskRegression model
baseHaz_cpp

C++ Fast Baseline Hazard Estimation
boot2pvalue

Compute the p.value from the distribution under H1
colCumProd

Apply cumprod in each column
SurvResponseVar

Extract the time and event variable from a Cox model
boxplot.Score

Boxplot risk quantiles
colCumSum

Apply cumsum in each column
confint.influenceTest

Confidence Intervals and Confidence Bands for the Difference Between Two Estimates
calcSeCSC

Standard error of the absolute risk predicted from cause-specific Cox models
confint.predictCox

Confidence Intervals and Confidence Bands for the predicted Survival/Cumulative Hazard
coxBaseEstimator

Extract the type of estimator for the baseline hazard
influenceTest

Influence test [Experimental!!]
information.wglm

Information for IPCW Logistic Regressions
colSumsCrossprod

Apply crossprod and colSums
ate

Average Treatment Effects Computation
as.data.table.predictCox

Turn predictCox Object Into a data.table
calcSeCox

Computation of standard errors for predictions
coxLP

Compute the linear predictor of a Cox model
ipcw

Estimation of censoring probabilities
coxModelFrame

Extract the design matrix used to train a Cox model
plotRisk

plot predicted risks
plotROC

Plot ROC curves
coxCenter

Extract the mean value of the covariates
confint.predictCSC

Confidence Intervals and Confidence Bands for the Predicted Absolute Risk (Cumulative Incidence Function)
coxFormula

Extract the formula from a Cox model
coef.CauseSpecificCox

Extract coefficients from a Cause-Specific Cox regression model
plotEffects

Plotting time-varying effects from a risk regression model.
confint.ate

Confidence Intervals and Confidence Bands for the Predicted Absolute Risk (Cumulative Incidence Function)
model.matrix.cph

Extract design matrix for cph objects
plot.riskRegression

Plotting predicted risk
coxVariableName

Extract variable names from a model
coxVarCov

Extract the variance covariance matrix of the beta from a Cox model
plotAUC

Plot of time-dependent AUC curves
model.matrix.phreg

Extract design matrix for phreg objects
colScale_cpp

Apply / by column
coxStrata

Define the strata for a new dataset
colMultiply_cpp

Apply * by column
coxN

Extract the number of observations from a Cox model
coxSpecial

Special characters in Cox model
print.CauseSpecificCox

Print of a Cause-Specific Cox regression model
predictCoxPL

Computation of survival probabilities from Cox regression models using the product limit estimator.
plotPredictRisk

Plotting predicted risks curves.
penalizedS3

S3-wrapper for S4 function penalized
print.FGR

Print of a Fine-Gray regression model
predict.riskRegression

Predict individual risk.
iid.wglm

IID for IPCW Logistic Regressions
coxStrataLevel

Returns the name of the strata in Cox model
print.influenceTest

Output of the DIfference Between Two Estimates
predictCox

Fast computation of survival probabilities, hazards and cumulative hazards from Cox regression models
print.ate

Print Average Treatment Effects
print.predictCSC

Print Predictions From a Cause-specific Cox Proportional Hazard Regression
rowSumsCrossprod

Apply crossprod and rowSums
print.predictCox

Print Predictions From a Cox Model
sampleData

Simulate data with binary or time-to-event outcome
iidCox

Extract iid decomposition from a Cox model
discreteRoot

Dichotomic search for monotone function
predictRisk

Extrating predicting risks from regression models
predict.CauseSpecificCox

Predicting Absolute Risk from Cause-Specific Cox Models
riskRegression

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

Input for data splitting algorithms
plotBrier

Plot Brier curve
plotCalibration

Plot Calibration curve
print.IPA

Print IPA object
predict.FGR

Predict subject specific risks (cumulative incidence) based on Fine-Gray regression model
simMelanoma

Simulate data alike the Melanoma data
rowCumProd

Apply cumprod in each row
rowCenter_cpp

Apply - by row
print.riskRegression

Print function for riskRegression models
rowMultiply_cpp

Apply * by row
subjectWeights

Estimation of censoring probabilities at subject specific times
rowCumSum

Apply cumsum in each row
sliceMultiply_cpp

Apply * by slice
summary.FGR

Summary of a Fine-Gray regression model
print.subjectWeights

Print subject weights
score.wglm

Score for IPCW Logistic Regressions
summary.Score

Summary of prediction performance metrics
selectCox

Backward variable selection in the Cox regression model
subsetIndex

Extract Specific Elements From An Object
wglm

Logistic Regression Using IPCW
print.Score

Print Score object
reconstructData

Reconstruct the original dataset
riskLevelPlot

Level plots for risk prediction models
simActiveSurveillance

Simulate data of a hypothetical active surveillance prostate cancer study
selectJump

Evaluate the influence function at selected times
riskRegression.options

Global options for riskRegression
sliceScale_cpp

Apply / by slice
splitStrataVar

Reconstruct each of the strata variables
terms.phreg

Extract terms for phreg objects
transformCIBP

Compute Confidence Intervals/Bands and P-values After a Transformation
rowPaste

Collapse Rows of Characters.
summary.ate

Summary Average Treatment Effects
rowScale_cpp

Apply / by row
summary.riskRegression

Summary of a risk regression model
Cforest

S3-wrapper function for cforest from the party package
Score

Score risk predictions
SmcFcs

SmcFcs
Paquid

Paquid sample
CSC

Cause-specific Cox proportional hazard regression
Melanoma

Malignant melanoma data