BoXHED2.0: Scalable Boosting of Dynamic Survival Analysis.

IF 8.1 2区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Statistical Software Pub Date : 2025-01-01 Epub Date: 2025-07-28 DOI:10.18637/jss.v113.i03

Arash Pakbin, Xiaochen Wang, Bobak J Mortazavi, Donald K K Lee

引用次数: 0

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 www.github.com/BoXHED.

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BoXHED2.0：动态生存分析的可扩展增强。

生存分析的现代应用越来越多地涉及时变协变量。Python包BoXHED2.0（带动态协变量的boosting eXact Hazard Estimator）是一个完全非参数的树推进式危险估计器，适用于比右审查更通用的生存设置，包括重复事件和竞争风险。BoXHED2.0还可以扩展到与参数增强生存模型相同的速度，部分原因是它的核心是用c++编写的，并且它还支持gpu和多核cpu的使用。BoXHED2.0可以从PyPI获得，也可以从www.github.com/BoXHED获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Statistical Software 工程技术-计算机：跨学科应用

CiteScore

10.70

自引率

1.70%

发文量

审稿时长

6-12 weeks

期刊介绍： The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.