在两阶段回归设置中对三明治方差估计的实际考虑.
American journal of epidemiology
|November 27, 2023
概括
这项研究引入了一个高效的三明治差异估计器,用于二阶段回归,特别有用的回归校准. 它为复杂的统计模型中差异估计提供了一个比引导式方法更快的计算替代方案.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 统计建模 统计建模
背景情况:
- 回归校准是处理共变量测量误差的常用方法.
- 在回归校准中,差异估计通常依赖于诸如引导式 (bootstrap) 等计算密集的重新采样方法.
- 三明治差异估计器提供了一个潜在的更快的替代方案,但需要专门的实施.
研究的目的:
- 介绍二阶段回归模型中三明治方差估计器的实用计算方法.
- 开发一个R实现的三明治差异估计适用于简单的随机样本和复杂的调查设计.
- 为了比较三明治差异估计器的性能与模拟和现实世界的数据中的引导方法.
主要方法:
- 概述了三明治方差估算器的计算步骤.
- 开发一个利用标准回归输出和现有包的R函数.
- 进行模拟研究和分析来自妇女健康倡议和西班牙裔社区健康研究/拉丁裔研究的数据.
主要成果:
- 在模拟中,三明治差异估计器表现出良好的数值性能.
- 简单的沃尔德启动链置信区间在特定场景中显示出不稳定性和过度覆盖,例如高协同变量相关性或实质性测量错误.
- 拟议的R方法为计算三明治方差估计提供了一种方便的方法.
结论:
- 三明治差异估计器是一种可行且计算效率高的替代方案,用于在2阶段回归和回归校准中进行差异估计.
- 开发的R方法简化了三明治差异估计的应用.
- 在具有高协同变量相关性或大测量误差的设置中使用引导式置信区间时,应谨慎使用.
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