ROC-guided survival trees and ensembles
The package is under active development.
You can install
rocTree from GitHub with:
## install.packages("devtools") devtools::install_github("stc04003/rocTree")
rocTree provides implementations to a unified framework for tree-structured analysis with censored survival outcomes. Different from many existing tree building algorithms, the
rocTree package incorporate time-dependent covariates by constructing a time-invariant partition scheme on the survivor population. The partition-based risk prediction function is constructed using an algorithm guided by the Receiver Operating Characteristic (ROC) curve. Specifically, the generalized time-dependent ROC curves for survival trees show that the target hazard function yields the highest ROC curve. 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. Moreover, the
rocTree package also offers a novel ensemble algorithm, where the ensemble is on unbiased martingale estimating equations.
Online document includes:
- Package vignette on simulating data used in examples.
- Package vignette on growing time-invariant survival trees.
- Package vignette on ensemble method.
Yifei Sun, Sy Han Chiou, Mei-Cheng Wang. ROC-Guided Survival Trees and Ensembles, (2019). doi: 10.1111/biom.13213.
rocTree package does not implement the works proposed by Drs. Hossain, Hassan, and Bailey (reference below), though they share similar names.
Hossain, MM; Hassan, MR; Bailey, J, ROC-tree: A novel decision tree induction algorithm based on receiver operating characteristics to classify gene expression data, (2008), 130, 2008, 2 pp. 455--465