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README.Rmd
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---
output: github_document
---
**rocTree**
---
[](http://www.repostatus.org/#active)
[](https://cran.r-project.org/)
[](https://cran.r-project.org/package=rocTree)
[](commits/master)
[](https://travis-ci.org/stc04003/rocTree)
[](https://ci.appveyor.com/project/stc04003/rocTree)
[)`-yellowgreen.svg)](/commits/master)
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
### ROC-guided survival trees and ensembles
***
### Development
The package is under active development.
### Installation
You can install `rocTree` from **GitHub** with:
```{r gh-installation, eval = FALSE}
## 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](https://www.sychiou.com/rocTree/index.html) includes:
* Package vignette on [simulating data used in examples](https://www.sychiou.com/rocTree/articles/rocTree-sim.html).
* Package vignette on [growing time-invariant survival trees](https://www.sychiou.com/rocTree/articles/rocTree-tree.html).
* Package vignette on [ensemble method](https://www.sychiou.com/rocTree/articles/rocTree-ensemble.html).
## Reference
Yifei Sun, Sy Han Chiou, Mei-Cheng Wang. ROC-Guided Survival Trees and Ensembles, \emph{Biometrics} (2019). [doi: 10.1111/biom.13213](https://www.ncbi.nlm.nih.gov/pubmed/31880315).
## 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