对面板二进制数据进行半参数回归分析,其故障时间取决于故障时间
Lei Ge1,2, Yang Li1, Jianguo Sun3
1Department of Biostatistics and Health Data Science, Indiana University School of Medicine and Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA.
Journal of applied statistics
|May 30, 2025
概括
这项研究引入了一种新的方法来分析反复事件数据,以解释依赖性故障时间,例如死亡. 这种方法改善了医院住院等健康事件的风险因素分析.
科学领域:
- 生物统计学 生物统计学
- 卫生研究方法论 卫生研究方法论
- 生存分析的分析.
背景情况:
- 面板二进制数据与反复事件在健康研究中很常见.
- 现有的方法往往无法解释依赖的故障时间 (例如死亡),从而削减了观测窗口.
- 医院住院数据的分析强调了需要采用适应复发和失效时间的方法.
研究的目的:
- 提出一种新的半参数联合建模程序,用于分析具有依赖失效时间的面板二进制数据.
- 解决一般化线性模型和现有文献在处理反复事件和失效时间同时处理的局限性.
- 为涉及纵向事件数据的健康和临床研究提供一个强大的统计框架.
主要方法:
- 开发了一种半参数联合建模方法.
- 实现了一个计算效率高的预期-最大化 (EM) 算法用于模型拟合.
- 为估计的一致性和异常正常性提供了理论保证,使得有效的统计推理成为可能.
主要成果:
- 拟议的EM算法提供了计算效率高的模型拟合.
- 从该方法中得出的估计结果被证明是一致的和异常正常的.
- 模拟研究验证了该方法在实际健康研究场景中的性能.
结论:
- 开发的联合建模程序有效地分析面板二进制数据与依赖失效时间.
- 该方法提供了一种统计学上合理的方法,用于在纵向健康研究中识别风险因素.
- 这项工作推进了在存在竞争性风险的情况下分析反复事件数据的研究,以住院数据分析为例.
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