Compares Cox and Survival Random Forests to Quantify Nonlinearity

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Documentation for package ‘survcompare’ version 0.1.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

cox_calibration_stats	Calibration stats of a fitted Cox PH model
linear_beta	Auxiliary function for simulatedata functions
predict.survensemble	Predicts event probability for a fitted survensemble
print.survcompare	Print survcompare object
print.survensemble	Prints trained survensemble object
print.survensemble_cv	Prints survensemble_cv object
simulate_crossterms	Simulated sample with survival outcomes with non-linear and cross-term dependencies
simulate_linear	Simulated sample with survival outcomes with linear dependencies
simulate_nonlinear	Simulated sample with survival outcomes with non-linear dependencies
srf_survival_prob_for_time	Internal function to compute survival probability by time from a fitted survival random forest
summary.survcompare	Summary of survcompare results
summary.survensemble	Prints summary of a trained survensemble object
summary.survensemble_cv	Prints a summary of survensemble_cv object
survcompare	Cross-validates and compares Cox Proportionate Hazards and Survival Random Forest models
survcoxlasso_train	Trains CoxLasso, using cv.glmnet(s="lambda.min")
survcox_cv	Cross-validates Cox or CoxLasso model
survcox_predict	Computes event probabilities from a trained cox model
survcox_train	Trains CoxPH using survival package, or trains CoxLasso (cv.glmnet, lambda.min), and then re-trains survival:coxph on non-zero predictors
survensemble_cv	Cross-validates predictive performance for Ensemble 1
survensemble_train	Fits an ensemble of Cox-PH and Survival Random Forest (SRF) with internal CV to tune SRF hyperparameters.
survival_prob_km	Calculates survival probability estimated by Kaplan-Meier survival curve Uses polynomial extrapolation in survival function space, using poly(n=3)
survsrf_cv	Cross-validates SRF model
survsrf_predict	Predicts event probability for a fitted SRF model
survsrf_train	Fits randomForestSRC, with tuning by mtry, nodedepth, and nodesize. Underlying model is by Ishwaran et al(2008) https://www.randomforestsrc.org/articles/survival.html Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. The Annals of Applied Statistics. 2008;2:841–60.
survsrf_tune	Internal function to tune SRF model, in nested CV loop
surv_brierscore	Calculates time-dependent Brier Score
surv_validate	Computes performance statistics for a survival data given the predicted event probabilities