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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

13,530

Version

2022.09.13

License

GPL (>= 2)

Issues

Pull Requests

Stars

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Maintainer

Thomas Alexander Gerds

Last Published

September 17th, 2022

Functions in riskRegression (2022.09.13)

IPA

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

Formula wrapper for crr from cmprsk
Melanoma

Malignant melanoma data
IC_Nelson_Aalen_cens_time

Influence function for Nelson-Aalen estimator.
Ctree

S3-Wrapper for ctree.
Score

Score risk predictions
CSC

Cause-specific Cox proportional hazard regression
SmcFcs

SmcFcs
Paquid

Paquid sample
as.data.table.predictCSC

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

Turn influenceTest Object Into a data.table
anova.ate

Risk Comparison Over Time
as.data.table.predictCox

Turn predictCox Object Into a data.table
as.data.table.ate

Turn ate Object Into a data.table
Cforest

S3-wrapper function for cforest from the party package
ate

Average Treatment Effects Computation
SurvResponseVar

Extract the time and event variable from a Cox model
SuperPredictor

Formula interface for SuperLearner::SuperLearner
autoplot.predictCSC

Plot Predictions From a Cause-specific Cox Proportional Hazard Regression
autoplot.predictCox

Plot Predictions From a Cox Model
colCenter_cpp

Apply - by column
autoplot.Score

ggplot AUC curve
coef.riskRegression

Extract coefficients from riskRegression model
baseHaz_cpp

C++ Fast Baseline Hazard Estimation
autoplot.ate

Plot Average Risks
coef.CauseSpecificCox

Extract coefficients from a Cause-Specific Cox regression model
boxplot.Score

Boxplot risk quantiles
boot2pvalue

Compute the p.value from the distribution under H1
calcSeCox

Computation of standard errors for predictions
calcSeCSC

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

Confidence Intervals and Confidence Bands for the Difference Between Two Estimates
confint.predictCSC

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

Apply * by column
colSumsCrossprod

Apply crossprod and colSums
colScale_cpp

Apply / by column
confint.predictCox

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

Apply cumprod in each column
coxStrataLevel

Returns the name of the strata in Cox model
coxModelFrame

Extract the design matrix used to train a Cox model
coxLP

Compute the linear predictor of a Cox model
coxStrata

Define the strata for a new dataset
colCumSum

Apply cumsum in each column
coxVarCov

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

Extract the mean value of the covariates
coxFormula

Extract the formula from a Cox model
confint.ate

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

Extract the number of observations from a Cox model
coxSpecial

Special characters in Cox model
coxBaseEstimator

Extract the type of estimator for the baseline hazard
ipcw

Estimation of censoring probabilities
iid.wglm

IID for IPCW Logistic Regressions
getSplitMethod

Input for data splitting algorithms
model.matrix.cph

Extract design matrix for cph objects
model.matrix.phreg

Extract design matrix for phreg objects
iidCox

Extract iid decomposition from a Cox model
information.wglm

Information for IPCW Logistic Regressions
influenceTest

Influence test [Experimental!!]
coxVariableName

Extract variable names from a model
plotEffects

Plotting time-varying effects from a risk regression model.
plotPredictRisk

Plotting predicted risks curves.
plotBrier

Plot Brier curve
plotRisk

plot predicted risks
plotCalibration

Plot Calibration curve
plotROC

Plot ROC curves
discreteRoot

Dichotomic search for monotone function
penalizedS3

S3-wrapper for S4 function penalized
print.influenceTest

Output of the DIfference Between Two Estimates
print.ate

Print Average Treatment Effects
predict.CauseSpecificCox

Predicting Absolute Risk from Cause-Specific Cox Models
predictCoxPL

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

Print of a Cause-Specific Cox regression model
print.FGR

Print of a Fine-Gray regression model
predict.FGR

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

Plot of time-dependent AUC curves
plot.riskRegression

Plotting predicted risk
riskLevelPlot

Level plots for risk prediction models
riskRegression.options

Global options for riskRegression
print.subjectWeights

Print subject weights
print.Score

Print Score object
print.IPA

Print IPA object
predictCox

Fast computation of survival probabilities, hazards and cumulative hazards from Cox regression models
rowCumProd

Apply cumprod in each row
print.predictCSC

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

Collapse Rows of Characters.
predict.riskRegression

Predict individual risk.
predictRisk

Extrating predicting risks from regression models
print.predictCox

Print Predictions From a Cox Model
rowCumSum

Apply cumsum in each row
print.riskRegression

Print function for riskRegression models
rowCenter_cpp

Apply - by row
reconstructData

Reconstruct the original dataset
score.wglm

Score for IPCW Logistic Regressions
rowMultiply_cpp

Apply * by row
rowSumsCrossprod

Apply crossprod and rowSums
simActiveSurveillance

Simulate data of a hypothetical active surveillance prostate cancer study
rowScale_cpp

Apply / by row
selectJump

Evaluate the influence function at selected times
selectCox

Backward variable selection in the Cox regression model
sliceScale_cpp

Apply / by slice
riskRegression

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

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

Extract Specific Elements From An Object
terms.phreg

Extract terms for phreg objects
splitStrataVar

Reconstruct each of the strata variables
wglm

Logistic Regression Using IPCW
simsynth

Simulating from a synthesized object
simMelanoma

Simulate data alike the Melanoma data
sliceMultiply_cpp

Apply * by slice
summary.ate

Summary Average Treatment Effects
subjectWeights

Estimation of censoring probabilities at subject specific times
summary.FGR

Summary of a Fine-Gray regression model
transformCIBP

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

Cooking and synthesizing survival data
summary.Score

Summary of prediction performance metrics
summary.riskRegression

Summary of a risk regression model