在使用多个参考调查的非概率样本中纠正参与偏差
Victoria Landsman1,2, Lingxiao Wang3, Ivan Carrillo-Garcia4
1Institute for Work and Health, Toronto, Canada.
Statistics in medicine
|February 23, 2026
概括
这项研究引入了一个新的框架,以减少使用多个参考样本的健康研究调查中的偏见. 拟议的校准估计器在参与机制未知的情况下提高了准确性.
科学领域:
- 调查方法调查方法.
- 统计推断的统计推断.
- 卫生研究 卫生研究
背景情况:
- 非概率抽样在健康研究中越来越多地使用.
- 这些样本中的参与机制通常是未知的,导致估计和关联的潜在偏差.
- 从非概率样本进行统计推断的现有方法仅限于单个参考样本.
研究的目的:
- 提出一个一般的框架来解决使用多个参考调查在非概率样本中的参与偏差.
- 扩大目前的统计推断能力,超出单一参考样本的限制.
- 专注于校准估计器,以实现实际实施和灵活性.
主要方法:
- 开发了一个通用框架,容纳多个参考调查.
- 专注于校准估计器,在框架内是一个灵活的特殊案例.
- 提出了两种差异估计方法:泰勒线性化和留下一个缺席的刀.
主要成果:
- 拟议的框架成功地解决了参与偏见的问题.
- 划分比率校准估计器显示了令人满意的表现,特别是分散参与概率.
- 使用拟议的方法,连续结果的差异估计明显较小.
结论:
- 新的框架和校准估计器有效地减轻了非概率样本中的偏差.
- 这些方法提供了实际优势,特别是在有限的微数据访问的情况下.
- 在对加拿大成年劳动者进行的现实研究中证明了实用性.
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