使用仪器变量对生存结果的因果效应的乘法与加法建模 - - 一个比较
Eleanor R John1, Michael J Crowther2, Vanessa Didelez3,4
1Department of Health Sciences, University of Leicester, UK.
Statistical methods in medical research
|December 11, 2024
概括
仪表变量 (IVs) 方法提供因果效应估计,但可能对数据生成机制敏感. 添加式IV方法在乘法模型下是有偏见的,而乘法IV则更强大,尽管可变性是一个问题.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 因果推理因果推理
背景情况:
- 仪表变量 (IVs) 方法越来越多地用于因果推断,特别是在存在未测量的混时.
- 现有的IV研究对于时间到事件的结果往往假设简单的数据生成机制 (DGM) 和有限的基线危险模型.
研究的目的:
- 为了比较添加与乘法仪表变量 (IV) 模型的性能,以获得时间到事件的结果.
- 评估不同IV方法对偏离假定的数据生成机制 (DGMs) 的敏感性.
主要方法:
- 模拟研究评估仪器变量 (IV) 方法的时间到事件结果.
- 根据各种数据生成机制 (DGMs) 进行加法和乘法IV模型的比较,包括从假设中偏离.
主要成果:
- 所有IV方法在存在未测量的混时都比天真估计器更好,除非IV是弱的.
- 添加式IV方法在乘法DGM下显示显著偏差,而乘法IV的敏感性较低.
- IV估计器可以比天真估计器显示出更高的平均平方误差,特别是在弱 IV,小样本大小和强烈的混的情况下.
结论:
- 与添加式IV模型相比,乘法IV模型表现出比假设的数据生成机制 (DGM) 的偏差更强的稳定性.
- 建议将生存概率与危险对比一起报告,以获得更可靠的因果解释.
- 意识到附加和乘法IV方法的局限性和敏感性对于现实数据分析至关重要.
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