@article{Chen2022, 
author = {Jia Chen and Renato De Leone},
title = {A survival tree for interval-censored failure time data},
year = {2022},
journal = {AIMS Mathematics},
volume = {7},
number = {10},
pages = {18099-18126},
keywords = {survival tree, interval-censored, conditional inference framework, hyper-parameter tuning, generalized log-rank tests},
url = {https://www.sciopen.com/article/10.3934/math.2022996},
doi = {10.3934/math.2022996},
abstract = {Interval-censored failure time data as a general type of survival data often arises in medicine and other applied fields. Survival tree is a flexible predictive method for survival data because no specific assumptions are required.Generalized Log-Rank Test have good power with parameters for interval-censored failure time data. We construct a special test statistic of Generalized Log-Rank Tests, and propose a new survival tree with hyper-parameter by combining the test statistic with Conditional Inference Framework for interval-censored failure time data. The effect of tuning hyper-parameter are discussed and hyper-parameter tuning allows the tree method to be more general and flexible. Thus the tree method either improve upon or remain competitive with existing tree method for interval-censored failure time data-ICtree, which is a special case of ours. An extensive simulation is executed to assess the predictive performance of our tree methods. Finally, the tree methods are applied to a tooth emergence data.}
}