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) <doi:10.48550/arXiv.1809.05627>.
Version: 1.1.1 Depends: R (≥ 3.5.0) Imports: DiagrammeR (≥ 1.0.0), data.tree (≥ 0.7.5), graphics, stats, survival (≥ 2.38), ggplot2, MASS, flexsurv, Rcpp LinkingTo: Rcpp, RcppArmadillo Published: 2020-08-01 DOI: 10.32614/CRAN.package.rocTree Author: Yifei Sun [aut], Mei-Cheng Wang [aut], Sy Han Chiou [aut, cre] Maintainer: Sy Han Chiou <schiou at utdallas.edu> BugReports: http://github.com/stc04003/rocTree/issues License: GPL (≥ 3) URL: http://github.com/stc04003/rocTree NeedsCompilation: yes Materials: NEWS CRAN checks: rocTree results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=rocTree to link to this page.
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