一个贝叶斯生存树危险模型,使用隐藏的高斯过程
Richard D Payne1, Nilabja Guha2, Bani K Mallick3
1Eli Lilly & Company, Lilly Corporate Center, Indianapolis, IN, 46285, United States.
Biometrics
|February 16, 2024
概括
我们介绍了一种灵活的贝叶斯模型,用于时间到事件数据分析. 这种新方法提供了明确的推断,可以识别患者子组和生物标志物,优于现有方法.
科学领域:
- 生物统计学 生物统计学
- 统计建模 统计建模
- 机器学习 机器学习
背景情况:
- 生存模型对于分析各种领域的时间到事件数据至关重要.
- 传统的比例危险模型提供了可解释性,但可能会违反假设.
- 非参数模型提供了灵活性,但往往缺乏强大的推理框架.
研究的目的:
- 提出一个新的贝叶斯树危险分区模型,将灵活性与时间到事件数据的明确推理框架相结合.
- 开发一种能够识别患者子组和预后/预测生物标志物的方法.
- 为了解决现有的生存分析技术的局限性.
主要方法:
- 建议使用贝叶斯树危险分区模型,利用潜在的高斯过程在分区内建模日志危险函数.
- 使用一个高效的可逆跳转马尔科夫链蒙特卡洛算法,通过拉普拉斯近似通过边缘化分区参数来实现.
- 拟议估计器的一致性属性在理论上已经确立.
主要成果:
- 拟议的模型展示了灵活性和推断能力,克服了现有方法的局限性.
- 该方法成功地在模拟数据和真实世界肝硬化数据集中识别了子组和生物标志物.
- 绩效是根据已建立的生存分析技术来评估的.
结论:
- 贝叶斯树危险分区模型为生存数据分析提供了一种强大而灵活的方法.
- 这种方法有助于发现患者子组和预测生物标志物,增强临床和研究应用.
- 开发的算法为复杂的生存数据提供了一个高效和统计学上合理的框架.
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