In stc04003/rocTree: Receiver Operating Characteristic (ROC)-Guided Classification and Survival Tree

rocTree

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

ROC-guided survival trees and ensembles

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Development

The package is under active development.

Installation

You can install rocTree from GitHub with:

## install.packages("devtools")
devtools::install_github("stc04003/rocTree")

Description

The 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 documentations

Online document includes:

• Package vignette on simulating data used in examples.
• Package vignette on growing time-invariant survival trees.
• Package vignette on ensemble method.

Reference

Yifei Sun, Sy Han Chiou, Mei-Cheng Wang. ROC-Guided Survival Trees and Ensembles, \emph{Biometrics} (2019). doi: 10.1111/biom.13213.

Disclaimer

The 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, \emph{Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining} (2008), \emph{Proceedings in Applied Mathematics} 130, 2008, 2 pp. 455--465

stc04003/rocTree documentation built on Sept. 25, 2020, 11:51 a.m.