用个人级别和聚合数据对潜在影响部分和可归因人口部分的非参数估计
Colleen E Chan1, Rodrigo Zepeda-Tello2, Dalia Camacho-García-Formentí3
1Department of Statistics and Data Science, Yale University, New Haven, Connecticut, USA.
Statistics in medicine
|August 13, 2025
概括
估计潜在影响分数 (PIF) 通常需要分配假设,如果违反,结果可能会偏差. 本研究引入了非参数方法,使用聚合或个人暴露数据准确估计PIF,改进公共卫生影响评估.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 使用连续暴露数据估计潜在影响分数 (PIF) 通常依赖于强烈的分布假设.
- 违反这些假设,特别是当风险分布未知时,可能导致偏差的PIF估计.
- 准确的PIF估计对于评估干预措施对公共卫生的影响至关重要.
研究的目的:
- 调查违反分布假设对PIF估计的影响.
- 提出新的非参数方法,以从聚合或个人曝光数据中估计PIF.
- 为在公共卫生研究中实施这些方法提供一个实际的工具.
主要方法:
- 该研究分析了分布违规对PIF估计的影响.
- 建议使用非参数方法进行PIF估计,使用聚合数据 (平均值和标准偏差) 和个人数据.
- 模拟研究是为了评估拟议方法的有限样本性能而进行的.
主要成果:
- 违反分布假设导致对人口影响率的偏见估计.
- 拟议的非参数方法在分布违规的情况下,与传统方法相比,提供了更准确的PIF估计.
- 模拟研究证明了非参数方法的有效性.
结论:
- 违反分布假设显著偏差潜在影响分数估计.
- 非参数方法为估计PIF提供了可靠的替代方案,特别是当风险分布未知或复杂时.
- 开发的R包"pifpaf"有助于应用这些方法进行公共卫生影响评估,例如评估减少含糖饮料消费对2型糖尿病发病率的影响.
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