rocTree-package: rocTree:Receiver Operating Characteristic (ROC)-Guided...

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

Description

The rocTree package uses a Receiver Operating Characteristic (ROC) guided classification algorithm to grow prune survival trees and ensemble.

Introduction

The rocTree package provides implementations to a unified framework for tree-structured analysis with censored survival outcomes. Different from many existing tree building algorithms, the rocTree package incorporates 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. 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 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.

Methods

The package contains functions to construct ROC-guided survival trees and ensemble through the main function rocTree.

Author(s)

Maintainer: Sy Han Chiou [e-mail]

Authors:

• Yifei Sun [e-mail]

• Mei-Cheng Wang [e-mail]

See Also

rocTree

rocTree documentation built on Aug. 1, 2020, 5:06 p.m.