export_Surv

Surv

<p>This function is imported from the <code>survival</code> package. See
<code><a rd-options="survival" href="/link/Surv?package=rocTree&version=1.1.1&to=survival" data-mini-rdoc="survival::Surv">Surv</a></code>.</p>

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) <arXiv:1809.05627>.

Sy  Chiou

rocTree

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

Yifei Sun

Mei-Cheng Wang

Sy Han Chiou

export_Surv function

<p>This function is imported from the <code>survival</code> package. See
<code><a rd-options='survival' href='Surv'>Surv</a></code>.</p>

export_Surv: `Surv` function imported from `survival`

Description

Arguments