R/rocTree_pkg.R
rocTree-package.RdThe rocTree package uses a Receiver Operating Characteristic (ROC) guided classification
algorithm to grow prune survival trees and ensemble.
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.
The package contains functions to construct ROC-guided survival trees and ensemble through
the main function rocTree.