具有多个时间尺度的平滑危险.
Angela Carollo1,2, Paul Eilers3, Hein Putter2
1Max Planck Institute for Demographic Research, Rostock, Germany.
Statistics in medicine
|December 9, 2024
概括
本研究引入了灵活的危险模型,用于分析具有多个时间尺度的时间到事件数据. 这些方法利用B-splines和处罚的Poisson概率进行准确的估计,并适应标准的审查和截断.
科学领域:
- 生物统计学 生物统计学
- 生存分析的分析.
- 统计建模 统计建模
背景情况:
- 时间到事件数据分析通常采用危险模型.
- 分析受多个时间尺度影响的事件需要专门的模型.
- 现有的模型可能缺乏灵活性来捕捉联合时间尺度的依赖性.
研究的目的:
- 开发灵活的非参数危险模型,用于多个相关时间尺度的时间到事件数据.
- 提出估计受联合时间尺度影响的危险表面的方法.
- 为了适应标准的数据结构,如右边审查和左边截断.
主要方法:
- 使用B-splines用于灵活规范多尺度危险表面.
- 使用处罚的波桑概率最大化来进行估计.
- 整合通用线性阵列模型 (GLAM) 算法或稀疏混合模型配方以实现高效的计算.
主要成果:
- 在模拟危险率的联合时间尺度依赖性方面表现出灵活性.
- 在拟议的模型中成功地适应了右翼审查和左翼截断.
- 为模型估计开发了高效的计算策略.
结论:
- 拟议的基于B-spline的危险模型提供了一个灵活和有效的工具,用于分析多个时间尺度的时间到事件数据.
- 这些方法在计算上是高效的,并处理常见的数据复杂性.
- 一个伴随的R-package促进了这些先进的生存分析技术的应用.
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