Non-Inferiority Test for Paired ROC Curves
Non-inferiority test and diagnostic test are very important in clinical trails.
This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test.

Receiver Operating Characteristic (ROC)-Guided Classification
and Survival Tree
c(person("Yifei", "Sun", email = "ys3072@cumc.columbia.edu", role = "aut"),
person("Mei-Cheng", "Wang", email = "mcwang@jhu.edu", role = "aut"),
person("Sy Han", "Chiou", email = "schiou@utdallas.edu", role = c("aut", "cre")))
Receiver Operating Characteristic (ROC)-guided survival trees and forests 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 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>.
DiagrammeR (>= 1.0.0), parallel, data.tree (>= 0.7.5),
graphics, stats, survival (>= 2.38), methods, tibble, dplyr,
ggplot2, MASS, flexsurv
Yifei Sun [aut],
Mei-Cheng Wang [aut],
Sy Han Chiou [aut, cre]

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>.
DiagrammeR (>= 1.0.0), data.tree (>= 0.7.5), graphics, stats,
survival (>= 2.38), tibble, dplyr, ggplot2, MASS, flexsurv,
Rcpp