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riskRegression (version 2018.04.21)

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

8,728

Version

2018.04.21

License

GPL (>= 2)

Maintainer

Thomas Alexander Gerds

Last Published

April 19th, 2018

Functions in riskRegression (2018.04.21)

SurvResponseVar

Extract the time and event variable from a Cox model

Extract the number of observations from a Cox model

as.data.table.predictCSC

Turn predictCSC object into a data.table

Plot predictions from a Cause-specific Cox proportional hazard regression

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

colMultiply_cpp

Apply * by column

Apply / by column

influenceCoxTest

Influence test [Experimental!!]

Compute the p.value from the distribution under H1

Boxplot risk quantiles

Define the strata for a new dataset

coxSpecialStrata

Special character for strata in Cox model

as.data.table.predictCox

Turn predictCox object into a data.table

Apply - by column

autoplot.predictCSC

Plot predictions from a Cause-specific Cox proportional hazard regression

autoplot.predictCox

Plot predictions from a Cox model

Computation of standard errors for predictions

Apply cumsum in each column

Plot ROC curves

Dichotomic search for monotone function

coxBaseEstimator

Extract the type of estimator for the baseline hazard

Extract the formula from a Cox model

Plotting time-varying effects from a risk regression model.

Extract the mean value of the covariates

Extract the design matrix used to train a Cox model

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

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

Estimation of censoring probabilities

coxVariableName

Extract variable names from a model

predict.riskRegression

Predict individual risk.

colSumsCrossprod

Apply crossprod and colSums

S3-wrapper for S4 function penalized

print.predictCSC

Print predictions from a Cause-specific Cox proportional hazard regression

model.matrix.phreg

Extract design matrix for phreg objects

print.predictCox

Print predictions from a Cox model

Evaluate the influence function at selected times

plot.riskRegression

Plotting predicted risk

Compute quantiles of a gaussian process

ggplot AUC curve

Simulate data alike the Melanoma data

Input for data splitting algorithms

Extract i.i.d. decomposition from a Cox model

Extract the information about the strata

Print average treatment effects

Extrating predicting risks from regression models

print.influenceCoxTest

Print the results of the influence test

plot predicted risks

print.riskRegression

Print function for riskRegression models

print.CauseSpecificCox

Print of a Cause-Specific Cox regression model

predict.CauseSpecificCox

Predicting absolute risk from cause-specific Cox models

Summary of a Fine-Gray regression model

summary.riskRegression

Summary of a risk regression model

print.subjectWeights

Print subject weights

rowSumsCrossprod

Apply crossprod and rowSums

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

Plot Brier curve

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

sliceMultiply_cpp

Apply * by slice

Apply cumsum in each row

plotCalibration

Plot Calibration curve

Apply / by slice

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

rowMultiply_cpp

Print of a Fine-Gray regression model

Print Score object

Simulate data with binary or time-to-event outcome

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

reconstructData

Reconstruct the original dataset

Backward variable selection in the Cox regression model

Reconstruct each of the strata variables

Estimation of censoring probabilities at subject specific times

Compute the average treatment effects via the g-formula

Cause-specific Cox proportional hazard regression

coef.CauseSpecificCox

Extract coefficients from a Cause-Specific Cox regression model

Formula wrapper for crr from cmprsk

coef.riskRegression

Extract coefficients from riskRegression model

Malignant melanoma data

Compute the linear predictor of a Cox model

Score risk predictions