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无残疾长寿的结果风险建模:随机森林和随机生存森林方法的比较
medRxiv : the preprint server for health sciences
|February 27, 2026
概括
在ASPREE试验中,随机生存森林 (RSF) 和随机森林 (RF) 在预测时间到事件结果方面表现相似. 在老年参与者的风险预测模型中,RSF的表现并不始终优于RF.
科学领域:
- 老年学是一门学科.
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 时间到事件数据分析通常采用包含时间的方法.
- 随机生存森林 (RSF) 是随机森林 (RF) 的延伸,旨在为此类数据设计.
- "阿司匹林降低老年人发作事件" (ASPREE) 试验为评估这些模型提供了一个队列.
研究的目的:
- 为了比较RSF和RF模型的预测性能.
- 确定RSF是否为时间到事件结果提供比RF更好的区分和校准.
- 评估将时间纳入风险预测模型的价值.
主要方法:
- 使用了ASPREE随机对照试验的数据.
- 不包括来自美国以外的参与者或缺少数据的参与者.
- 训练随机森林 (RF) 和随机生存森林 (RSF) 模型在2,291名参与者身上,使用了115个候选预测因素.
- 主要结局是事件痴呆症,身体残疾或死亡的最早发生.
主要成果:
- 主要终点发生在10.5%的参与者身上.
- 两种RF和RSF模型都显示了类似的歧视指标 (灵敏度,特异性,PPV,时间依赖的AUC,Harrell的一致性).
- 根据Brier分数评估的校准也在两种模型之间是可比的.
结论:
- 随机生存森林 (RSF) 和随机森林 (RF) 在这个队列中表现出相似的歧视和校准.
- RSF可能不会始终提供比RF更准确的结果预测.
- 需要在多样化的临床试验队列中进行进一步的研究,以确定基于时间的风险建模提供附加值的特定背景.
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