Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks
Risk Comparison Over Time
Turn ate Object Into a data.table
Turn influenceTest Object Into a data.table
Turn predictCox Object Into a data.table
Turn predictCSC Object Into a data.table
Average Treatment Effects Computation
Plot Average Risks
Plot Predictions From a Cox Model
Plot Predictions From a Cause-specific Cox Proportional Hazard Regress...
ggplot AUC curve
C++ Fast Baseline Hazard Estimation
Compute the p.value from the distribution under H1
Boxplot risk quantiles
Computation of standard errors for predictions
Standard error of the absolute risk predicted from cause-specific Cox ...
S3-wrapper function for cforest from the party package
Estimated Average Treatment Effect.
Extract coefficients from a Cause-Specific Cox regression model
Extract coefficients from riskRegression model
Estimates from IPCW Logistic Regressions
Apply - by column
Apply cumsum in each column
Apply * by column
Apply / by column
Confidence Intervals and Confidence Bands for the average treatment ef...
Confidence Intervals and Confidence Bands for the Difference Between T...
Confidence Intervals and Confidence Bands for the predicted Survival/C...
Confidence Intervals and Confidence Bands for the Predicted Absolute R...
Confidence intervals for Estimate from IPCW Logistic Regressions
Extract the type of estimator for the baseline hazard
Extract the mean value of the covariates
Extract the formula from a Cox model
Compute the linear predictor of a Cox model
Extract the design matrix used to train a Cox model
Extract the number of observations from a Cox model
Special characters in Cox model
Define the strata for a new dataset
Returns the name of the strata in Cox model
Extract the variance covariance matrix of the beta from a Cox model
Extract variable names from a model
Cause-specific Cox proportional hazard regression
S3-Wrapper for ctree.
Dichotomic search for monotone function
Formula wrapper for crr from cmprsk
Input for data splitting algorithms
Formula interface for glmnet
Fitting HAL for use with predictRisk
IID for IPCW Logistic Regressions
Extract iid decomposition from a Cox model
Influence test [Experimental!!]
Information for IPCW Logistic Regressions
Explained variation for settings with binary, survival and competing r...
Estimation of censoring probabilities
Check Computation of the Influence Function in a Cox Model
Extract design matrix for cph objects
Extract design matrix for phreg objects
Statistical Inference for the Average Treatment Effect
Statistical Inference for Estimate from IPCW Logistic Regressions
S3-wrapper for S4 function penalized
Plotting predicted risk
Plot of time-dependent AUC curves
Plot Brier curve
Plot Calibration curve
Plotting time-varying effects from a risk regression model.
Plotting predicted risks curves.
plot predicted risks
Plot ROC curves
Predicting Absolute Risk from Cause-Specific Cox Models
Predict subject specific risks (cumulative incidence) based on Fine-Gr...
Predict individual risk.
Survival probabilities, hazards and cumulative hazards from Cox regres...
Deprecated Function for Product Limit Estimation of Survival Probabili...
Extrating predicting risks from regression models
Print Average Treatment Effects
Print of a Cause-Specific Cox regression model
Print of a Fine-Gray regression model
Print of a glmnet regression model
Output of the DIfference Between Two Estimates
Print IPA object
Print Predictions From a Cox Model
Print Predictions From a Cause-specific Cox Proportional Hazard Regres...
Print function for riskRegression models
Print Score object
Print subject weights
Print synthesized code
Reconstruct the original dataset
Level plots for risk prediction models
Risk Regression Models and Prediction Scores for Survival Analysis wit...
Global options for riskRegression
Risk Regression Fits a regression model for the risk of an event -- al...
Apply - by row
Apply cumsum in each row
Apply * by row
Collapse Rows of Characters.
Apply / by row
Apply crossprod and rowSums
Simulate data with binary or time-to-event outcome
Save confidential Cox objects
Export a synth object.
Score risk predictions
Score for IPCW Logistic Regressions
Backward variable selection in the Cox regression model
Evaluate the influence function at selected times
Simulate data of a hypothetical active surveillance prostate cancer st...
Simulate data alike the Melanoma data
simulating data alike the pbc data
Simulating from a synthesized object
SmcFcs
Reconstruct each of the strata variables
Estimation of censoring probabilities at subject specific times
Extract Specific Elements From An Object
Summary Average Treatment Effects
Summary of a Fine-Gray regression model
Summary of a risk regression model
Summary of prediction performance metrics
Formula interface for SuperLearner::SuperLearner
Extract the time and event variable from a Cox model
Cooking and synthesizing survival data
Extract terms for phreg objects
Compute Confidence Intervals/Bands and P-values After a Transformation
Variance-Covariance Matrix for the Average Treatment Effect.
Variance-covariance for IPCW Logistic Regressions
Extract IPCW Weights
Logistic Regression Using IPCW
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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