对左截止右截止竞争风险数据的累积发病率函数的回归建模:修改后的伪观测方法
Rong Rong1, Jing Ning2, Hong Zhu3
1Department of Statistical Science, Southern Methodist University, Dallas, Texas 75275.
Communications in statistics: theory and methods
|September 2, 2025
概括
这项研究引入了一种新的伪观察 (PO) 方法,用于分析复杂数据的累积发病率函数 (CIF),改善左截断和右截断的竞争风险的统计建模. 这种方法处理了一般的截断和审查,在医学研究中提供了更广泛的应用.
科学领域:
- 生物统计学
- 生存分析
- 流行病学
背景情况:
- 现有的累积发病率函数 (CIF) 的统计方法与左截断和右截断的竞争风险数据通常依赖于复杂的方程和独立的审查/截断假设.
- 伪观测 (PO) 方法已经显示出对右截止数据的CIF回归有希望,但其扩展到左截止数据是有限的.
研究的目的:
- 扩展伪观测 (PO) 方法用于累积发病率函数 (CIF) 的回归建模,同时存在左截断和右截断.
- 开发一种适应总体截断和审查机制的方法,包括共变量依赖的场景.
主要方法:
- 该研究建议使用伪观测 (PO) 来直接建模CIF,用于左截断和右截断的竞争风险数据.
- 通过将共变量调整的权重纳入CIF的逆概率加权 (IPW) 估计器来解决共变量依赖的截断和审查.
- 建议的估计器的大样本属性得出,并通过模拟研究评估有限样本的性能.
主要成果:
- 拟议的伪观测 (PO) 方法有效地模拟了总结和审查条件下的累积发病率函数 (CIF).
- 反向概率加权 (IPW) 估计器,调整为共变量依赖的截断/审查,在模拟中显示出强大的性能.
- 该方法已成功应用于真实世界队列研究,涉及孕妇接触氨酸衍生物.
结论:
- 扩展伪观测 (PO) 方法为分析具有竞争风险的复杂生存数据,左切割和右审查提供了灵活和强大的框架.
- 这种方法放松了限制性的独立性假设,提高了流行病学和临床研究中的统计推断的可靠性.
- 这些发现为处理各种科学领域的时间到事件数据的研究人员提供了宝贵的工具.
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