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riskRegression (version 2020.02.05)

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

9,120

Version

2020.02.05

License

GPL (>= 2)

Maintainer

Thomas Alexander Gerds

Last Published

February 8th, 2020

Functions in riskRegression (2020.02.05)

Formula interface for SuperLearner::SuperLearner

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

S3-Wrapper for ctree.

Malignant melanoma data

Plot Average Risks

ggplot AUC curve

Compute the Average Treatment Effects Via

Formula wrapper for crr from cmprsk

as.data.table.predictCox

Turn predictCox Object Into a data.table

autoplot.predictCSC

Plot Predictions From a Cause-specific Cox Proportional Hazard Regression

autoplot.predictCox

Plot Predictions From a Cox Model

Score risk predictions

Apply cumsum in each column

Compute the p.value from the distribution under H1

Boxplot risk quantiles

colMultiply_cpp

Apply * by column

Extract the mean value of the covariates

coef.CauseSpecificCox

Extract coefficients from a Cause-Specific Cox regression model

as.data.table.influenceTest

Turn influenceTest Object Into a data.table

Cause-specific Cox proportional hazard regression

S3-wrapper function for cforest from the party package

confint.influenceTest

Confidence Intervals and Confidence Bands for the Difference Between Two Estimates

model.matrix.phreg

Extract design matrix for phreg objects

predict.riskRegression

Predict individual risk.

Compute the linear predictor of a Cox model

confint.predictCSC

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

S3-wrapper for S4 function penalized

Extract the design matrix used to train a Cox model

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

SurvResponseVar

Extract the time and event variable from a Cox model

print.predictCSC

Print Predictions From a Cause-specific Cox Proportional Hazard Regression

coef.riskRegression

Extract coefficients from riskRegression model

as.data.table.ate

Turn ate Object Into a data.table

confint.predictCox

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

Extract iid decomposition from a Cox model

Apply - by column

Define the strata for a new dataset

Compute quantiles of a gaussian process

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

Returns the name of the strata in Cox model

Apply cumprod in each column

as.data.table.predictCSC

Turn predictCSC Object Into a data.table

Extract the formula from a Cox model

print.predictCox

Print Predictions From a Cox Model

print.riskRegression

Print function for riskRegression models

print.subjectWeights

Print subject weights

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

predict.CauseSpecificCox

Predicting Absolute Risk from Cause-Specific Cox Models

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

Influence test [Experimental!!]

riskRegression.options

Global options for riskRegression

Collapse Rows of Characters.

Reconstruct each of the strata variables

Dichotomic search for monotone function

Extract the number of observations from a Cox model

Special characters in Cox model

coxBaseEstimator

Extract the type of estimator for the baseline hazard

Plot ROC curves

plotCalibration

Plot Calibration curve

plot predicted risks

Plot Brier curve

Estimation of censoring probabilities at subject specific times

simActiveSurveillance

Simulate data of a hypothetical active surveillance prostate cancer study

Simulate data alike the Melanoma data

Compute Influence Functions after Transformation

Input for data splitting algorithms

Summary Average Treatment Effects

Summary of prediction performance metrics

print.influenceTest

Output of the DIfference Between Two Estimates

Apply / by slice

sliceMultiply_cpp

Apply * by slice

Print Average Treatment Effects

Apply cumprod in each row

Compute P-values After a Transformation

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

Apply / by column

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

Computation of standard errors for predictions

colSumsCrossprod

Apply crossprod and colSums

Compute Confidence Intervals using a transformation

Compute Standard Errors after Transformation

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

coxVariableName

Extract variable names from a model

Plot of time-dependent AUC curves

plot.riskRegression

Plotting predicted risk

Estimation of censoring probabilities

model.matrix.cph

Extract design matrix for cph objects

plotPredictRisk

Plotting predicted risks curves.

Print IPA object

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

Plotting time-varying effects from a risk regression model.

Print of a Fine-Gray regression model

print.CauseSpecificCox

Print of a Cause-Specific Cox regression model

reconstructData

Reconstruct the original dataset

Print Score object

Extrating predicting risks from regression models

Level plots for risk prediction models

rowSumsCrossprod

Apply crossprod and rowSums

Apply cumsum in each row

rowMultiply_cpp

Simulate data with binary or time-to-event outcome

Extract terms for phreg objects

summary.riskRegression

Summary of a risk regression model

Backward variable selection in the Cox regression model

Extract Specific Elements From An Object

Evaluate the influence function at selected times

Summary of a Fine-Gray regression model