Receiver Operating Characteristic (ROC)-Guided Classification and Survival Tree

Receiver Operating Characteristic (ROC)-guided survival trees and ensemble algorithms are implemented, providing a unified framework for tree-structured analysis with censored survival outcomes. A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard/survival function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) .


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install.packages("rocTree")

1.1.0 by Sy Han Chiou, 6 days ago


http://github.com/stc04003/rocTree


Report a bug at http://github.com/stc04003/rocTree/issues


Browse source code at https://github.com/cran/rocTree


Authors: Yifei Sun [aut] , Mei-Cheng Wang [aut] , Sy Han Chiou [aut, cre]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports DiagrammeR, data.tree, graphics, stats, survival, tibble, dplyr, ggplot2, MASS, flexsurv, Rcpp

Linking to Rcpp, RcppArmadillo


See at CRAN