用于追溯队列数据的分布式滞后模型,适用于建筑环境和体重研究
Jennifer F Bobb1,2, Stephen J Mooney3, Maricela Cruz1,2
1Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, United States.
Biometrics
|January 24, 2025
概括
使用分布式滞后模型 (DLM) 的全队列方法克服了回顾性研究中的挑战. 这些方法有效地估计了长时间暴露对健康的影响,提高了准确性和功率.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 分布滞后模型 (DLM) 对于对随时间的暴露对健康的影响进行时间序列分析至关重要.
- 由于电子健康记录中的暴露史不完整,将DLM应用于回顾性队列研究是困难的.
- 使用子队列的现有方法限制了统计能力,并可能引入选择偏差.
研究的目的:
- 开发和评估在回顾性队列研究中应用DLM的全队列方法.
- 为了应对参与者之间不一致的暴露历史长度所带来的挑战.
- 通过使用所有可用的数据,使健康影响能够在尽可能长的时间延迟中估计.
主要方法:
- 建议使用多重归算的全队列方法来重建完整的暴露历史.
- 进行模拟研究,以比较全队列方法与传统的子队列方法.
- 将开发的方法应用于回顾性队列研究,对12年来居住密度和体重进行检查.
主要成果:
- 模拟研究表明,子队列限制可能会因混而导致暴露效应估计偏差.
- 具有多重归算的全队列方法有效地减轻了偏差,并有效地估计了滞后和累积效应.
- 对居住密度和体重的分析显示了立即影响 (1-2年前) 和最大滞后 (12年前) 的影响.
结论:
- 全队列分布式滞后模型为回顾性队列研究提供了一种高效和公正的方法.
- 这些方法提高了识别关键暴露窗口和估计长期健康影响的能力.
- 这些发现支持在回顾性设计中使用DLM来进行可靠的流行病学研究.
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