<b>BoXHED2.0</b>: Scalable Boosting of Dynamic Survival Analysis

Modern applications of survival analysis increasingly involve time-dependent covariates.The Python package BoXHED2.0 (Boosted eXact Hazard Estimator with Dynamic covariates) is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from https://github.com/BoXHED.

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Publication Details

Journal
Journal of Statistical Software
Published
2025-01-01
DOI
https://doi.org/10.18637/jss.v113.i03
Primary Topic
Machine Learning in Materials Science
Type
article
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article

BoXHED2.0: Scalable Boosting of Dynamic Survival Analysis

Donald K. K. Lee, Arash Pakbin, Bobak J. Mortazavi, Xiaochen Wang
Journal of Statistical Software
Machine Learning in Materials Science
article

BoXHED2.0: Scalable Boosting of Dynamic Survival Analysis

Donald K. K. Lee, Arash Pakbin, Bobak J. Mortazavi, Xiaochen Wang
article en

Abstract

Modern applications of survival analysis increasingly involve time-dependent covariates.The Python package BoXHED2.0 (Boosted eXact Hazard Estimator with Dynamic covariates) is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from https://github.com/BoXHED.

Journal of Statistical SoftwareVol. 113(3)
Emory University (US), Mitchell Institute (US), Yale University (US), Texas A&M University (US)
Openalex Percentile: Top 86%
Machine Learning in Materials Science
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<b>BoXHED2.0</b>: Scalable Boosting of Dynamic Survival Analysis — Donald K. K. Lee, Arash Pakbin, et al. · Journal of Statistical Software (2025) | TGRS Research Map | TGRS