在纵向研究中校准不同的设备时,是否应该使用回归校准或多重归算?
Matthew Shane Loop1, Sarah C Lotspeich2, Tanya P Garcia3
1Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, Auburn, AL 36849, United States.
American journal of epidemiology
|July 3, 2024
概括
在纵向研究中,使用新的测量设备可能会引入错误. 多重归算与预测平均值匹配最好处理这些错误和缺失的数据,优于回归校准.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 纵向数据分析 纵向数据分析
背景情况:
- 纵向研究中的测量设备在访问之间可能会发生变化.
- 校准研究评估设备之间的差异,但引入缺失的数据.
- 统计调整对于准确的纵向数据分析至关重要.
研究的目的:
- 为了比较回归校准和多重归算,在纵向研究中调整器件之间的差异.
- 评估在校准研究中处理缺失数据的方法.
- 为了确定最可靠的统计方法,用于纵向脉冲波速度研究与设备变化.
主要方法:
- 使用线性回归进行了模拟研究.
- 场景模仿了现实世界的纵向研究,特别是脉冲波速度.
- 进行了回归校准和多重归算 (完全随机和预测平均值匹配) 的比较.
主要成果:
- 无论是回归校准还是多重归算,都在很大程度上是公正的.
- 估计标准错误对这两种方法都带来了挑战.
- 多重归算与预测平均值匹配密切匹配的经验标准错误.
- 完全随机的多重归算低估了标准误差高达50%.
- 使用启动式标准错误进行回归校准,与完全随机归算相比,显示了适度的改进.
- 回归校准比多重归算方法更有效.
结论:
- 对于具有潜在设备相关错误的纵向研究,建议使用预测平均值匹配的多重归算.
- 完全随机归算和回归校准可能导致不准确的标准误差估计.
- 准确的调整测量错误和缺失的数据在纵向研究中至关重要.
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