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riskRegression (version 2019.11.03)

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

Description

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.

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Version

Install

install.packages('riskRegression')

Monthly Downloads

16,575

Version

2019.11.03

License

GPL (>= 2)

Maintainer

Thomas Alexander Gerds

Last Published

November 4th, 2019

Functions in riskRegression (2019.11.03)

SurvResponseVar

Extract the time and event variable from a Cox model

colMultiply_cpp

Apply * by column

Apply cumsum in each column

as.data.table.ate

Turn ate Object Into a data.table

Apply - by column

as.data.table.predictCox

Turn predictCox Object Into a data.table

Compute quantiles of a gaussian process

Apply cumprod in each column

Compute the Average Treatment Effects Via

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

ggplot AUC curve

as.data.table.influenceTest

Turn influenceTest Object Into a data.table

Score risk predictions

Plot Average Risks

plot.riskRegression

Plotting predicted risk

Extract the formula from a Cox model

autoplot.predictCox

Plot Predictions From a Cox Model

Extract the mean value of the covariates

autoplot.predictCSC

Plot Predictions From a Cause-specific Cox Proportional Hazard Regression

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

Returns the name of the strata in Cox model

as.data.table.predictCSC

Turn predictCSC Object Into a data.table

Estimation of censoring probabilities

Define the strata for a new dataset

Plot of time-dependent AUC curves

coxVariableName

Extract variable names from a model

model.matrix.phreg

Extract design matrix for phreg objects

Compute the p.value from the distribution under H1

coxBaseEstimator

Extract the type of estimator for the baseline hazard

Boxplot risk quantiles

confint.predictCox

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

Special characters in Cox model

Extract the number of observations from a Cox model

Plotting time-varying effects from a risk regression model.

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

plotPredictRisk

Plotting predicted risks curves.

Computation of standard errors for predictions

Apply / by column

Print IPA object

S3-wrapper for S4 function penalized

model.matrix.cph

Extract design matrix for cph objects

coef.CauseSpecificCox

Extract coefficients from a Cause-Specific Cox regression model

confint.influenceTest

Confidence Intervals and Confidence Bands for the Difference Between Two Estimates

Dichotomic search for monotone function

coef.riskRegression

Extract coefficients from riskRegression model

confint.predictCSC

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

Plot ROC curves

plot predicted risks

Print Score object

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

Input for data splitting algorithms

Print Average Treatment Effects

Compute the linear predictor of a Cox model

Plot Brier curve

Extract the design matrix used to train a Cox model

plotCalibration

Plot Calibration curve

colSumsCrossprod

Apply crossprod and colSums

print.influenceTest

Output of the DIfference Between Two Estimates

Extrating predicting risks from regression models

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

rowSumsCrossprod

Apply crossprod and rowSums

predict.CauseSpecificCox

Predicting Absolute Risk from Cause-Specific Cox Models

Summary Average Treatment Effects

Collapse Rows of Characters.

Simulate data alike the Melanoma data

simActiveSurveillance

Simulate data of a hypothetical active surveillance prostate cancer study

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

print.predictCSC

Print Predictions From a Cause-specific Cox Proportional Hazard Regression

print.CauseSpecificCox

Print of a Cause-Specific Cox regression model

riskRegression.options

Global options for riskRegression

Extract iid decomposition from a Cox model

Simulate data with binary or time-to-event outcome

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

Compute Influence Functions after Transformation

Influence test [Experimental!!]

print.predictCox

Print Predictions From a Cox Model

Apply cumsum in each row

Backward variable selection in the Cox regression model

rowMultiply_cpp

Compute P-values After a Transformation

Evaluate the influence function at selected times

summary.riskRegression

Summary of a risk regression model

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

predict.riskRegression

Predict individual risk.

Compute Standard Errors after Transformation

reconstructData

Reconstruct the original dataset

Print of a Fine-Gray regression model

Level plots for risk prediction models

print.subjectWeights

Print subject weights

print.riskRegression

Print function for riskRegression models

Apply cumprod in each row

Apply / by slice

Compute Confidence Intervals using a transformation

sliceMultiply_cpp

Apply * by slice

Extract terms for phreg objects

Summary of a Fine-Gray regression model

Reconstruct each of the strata variables

Estimation of censoring probabilities at subject specific times

Extract Specific Elements From An Object

S3-wrapper function for cforest from the party package

Formula interface for SuperLearner::SuperLearner

Cause-specific Cox proportional hazard regression

Malignant melanoma data

Formula wrapper for crr from cmprsk

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

S3-Wrapper for ctree.