开发非响应权重,以解释在纵向妊娠队列中的与磨损相关的偏差
Tona M Pitt1,2, Erin Hetherington3, Kamala Adhikari4,2
1Department of Paediatrics, University of Calgary, 28 Oki Drive NW, Calgary, T3B 6A8, Canada.
BMC medical research methodology
|December 15, 2023
概括
反向概率权重可以减少纵向研究中的磨损偏差. 拉索方法比先验选择更有效地产生权重,以减轻怀孕队列中的非响应偏差.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 纵向研究设计 纵向研究设计
背景情况:
- 未来的队列研究面临着消耗偏差,影响数据有效性.
- 反向概率权衡是一种统计方法,用于解决磨损偏差.
- 这项研究利用了
- 我们所有的家庭.
- 纵向怀孕队列 (3351对母婴对).
研究的目的:
- 开发和评估反向概率权重以减轻磨损偏差.
- 为了比较不同变量选择方法 (先验与LASSO) 在重量生成中的有效性.
- 为了评估减轻现实世界纵向妊娠队列偏差的体重表现.
主要方法:
- 采用了两种可变选择方法:基于先验的知识和最小绝对收缩和选择操作员 (LASSO).
- 后勤回归模型预测了研究的延续,模型性能使用AUROC和校准图进行评估.
- 产生了稳定的逆概率权重,并通过基线特征的标准化差异来评估其性能.
主要成果:
- 既先验模型和LASSO模型都表现出良好的公平歧视 (分别为AUROC 0.69和0.73) 并得到了良好的校准.
- 未加权的数据显示了15个人口变量中的实质性标准化差异 (>10%).
- 反向概率权重显著减少了标准化差异,LASSO衍生权重显示出较大的改善 (最大差异为5%),相比先验权重 (13%).
结论:
- 与知识驱动的先验方法相比,LASSO变量选择方法在解决非响应偏差方面产生了更强大的权重.
- 这些开发的权重适用于多个纵向数据收集波,以有效减少偏差.
- 该研究强调了LASSO在纵向研究中创造可靠的权重的实用性.
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