聚类生存数据的新方法:对治疗效应异质性和选择变量的估计
1Department of Biostatistics and Epidemiology, Rutgers University, Piscataway, New Jersey, USA.
Biometrical journal. Biometrische Zeitschrift
|December 10, 2023
概括
我们开发了一种新的因果推断方法,即riAFT-BART,用于估计治疗效果,并确定具有不同结果的患者子组. 这种方法增强了对复杂健康数据集中的治疗异质性的理解.
科学领域:
- 生物统计学 生物统计学
- 机器学习 机器学习
- 因果推理因果推理
背景情况:
- 聚类和审查的生存数据对因果推断提出了挑战.
- 现有的方法经常与多层结构扎,并识别治疗效果异质性.
- 复杂的生存数据分析需要灵活的建模技术.
研究的目的:
- 引入和验证随机截取加速失效时间模型与贝叶斯增量回归树 (riAFT-BART) 进行因果推理.
- 证明riAFT-BART在估计治疗效果异质性和执行变量选择方面的实用性.
- 在集群生存分析中使用新的归算策略来解决缺失的数据.
主要方法:
- 在多层次,集群和审查的生存数据中开发了riAFT-BART模型用于因果推断.
- 利用基于概率的机器学习来绘制后部样本以进行个人治疗效果估计.
- 提出了一种基于 permutation 的变量选择方法,以及对不完整数据的引导式归算策略.
主要成果:
- 模拟研究证实,在各种数据场景中,拟议方法的性能优于现有方法.
- 已证明能够识别具有差异治疗效果的子群体.
- 成功地将方法应用于COVID-19患者数据,用于死亡率预测和药物效应分析.
结论:
- riAFT-BART为因果推断,治疗效果异质性估计和复杂生存数据中的变量选择提供了一个强大的框架.
- 提出的方法有效地处理聚类,审查和不完整的生存数据.
- 这些方法在可访问的R包中实现,以便在健康研究中得到更广泛的应用.
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