随机生存森林用于对正确审查数据的反复事件的分析,有或没有终端事件
Juliette Murris1,2, Olivier Bouaziz3, Michal Jakubczak4,5
1HeKA, Inria, Inserm, Centre de recherche des Cordeliers, Université Paris Cité, Paris, France. murris.juliette@gmail.com.
BMC medical research methodology
|November 21, 2025
概括
RecForest是一种用于反复事件数据的新方法,它扩展了用于医学研究的随机生存森林 (RSF). 它准确地模拟复杂的生存结果,在分析中超越现有方法.
科学领域:
- 生物统计学 生物统计学
- 机器学习 机器学习
- 生存分析的分析.
背景情况:
- 随机生存森林 (RSF) 在医学研究中对复杂的生存结果建模有用.
- 现有的RSF方法不能充分处理与反复事件 (RE) 相关的右边审查数据.
研究的目的:
- 介绍RecForest,这是RSF的一个新型扩展,专门设计用于反复事件数据.
- 适应RSF方法论,以处理医学研究中时间到复发数据的复杂性.
主要方法:
- RecForest使用伪得分或沃尔德测试从RE的Ghosh-Lin模型调整了RSF分割规则.
- 汇总估计汇总来自单个树的预期事件计数.
- 性能是使用专门的一致性指数 (C指数) 和RE数据的平均平方误差 (MSE) 进行评估的.
主要成果:
- 在模拟和现实世界数据应用中,RecForest展示了卓越的性能.
- 达到0.60和0.82之间的C指数值,表明强大的预测准确性.
- 与非参数平均累积函数和Ghosh-Lin模型相比,MSE指标始终较低.
结论:
- RecForest提供了一种有价值的新工具,用于分析医疗研究中至关重要的时间到复发数据.
- 该方法增强了复杂的生存数据的分析能力,这些复杂的生存数据具有反复的事件.
- RecForest的R包实现在CRAN上可供公众使用.
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