基于潜在特征的测量作为数据协调和缺失数据解决方案,应用于对儿童健康结果的环境影响的队列
Emily A Knapp1, Amii M Kress1, Ronel Ghidey1
1From the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Epidemiology (Cambridge, Mass.)
|January 30, 2025
概括
罗塞塔 (ROSETTA) 是一种潜在变量方法,成功地在不同群体中创建了共同的社会经济地位 (SES) 衡量标准. 这种方法通过最大限度地利用可用的信息和减少缺失数据的挑战来增强聚合数据分析.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 协作研究联盟提高了亚组和罕见结果分析的样本规模.
- 跨队列研究由于设计和测量不同而面临独特的缺失数据挑战.
- 跨异质数据集的标准化措施对于强大的聚合分析至关重要.
研究的目的:
- 扩展ROSETTA隐性变量方法,在不同的群体中创建一个共同的社会经济地位 (SES) 衡量标准.
- 通过协调SES的不同指标来解决合作研究中的缺失数据挑战.
- 为了促进在儿童健康结果环境影响 (ECHO) 队列内汇集的个人参与者数据分析.
主要方法:
- 罗塞塔方法应用于53个队列中的39372名参与者的52个产前SES指标.
- 隐性变量建模被用来从部分重叠的措施中推导出共同的SES因子.
- 使用因子分析来评估衍生SES指标的合适性,有效性和相关性.
主要成果:
- 罗塞塔产生了三个SES因素:收入和教育,保险和贫困,以及失业.
- 对于很大一部分参与者来说,可获得因子得分,超过单个指标的可用性.
- 更高的SES,特别是收入和教育因素,与更低的产前吸烟几率 (OR:0.42) 有关.
- 该方法证明了数据的良好匹配,内容的有效性和与现有SES措施的强烈相关性.
结论:
- 罗塞塔有效地在一个共同的尺度上创建了协调的措施,用于对个人参与者数据的聚合分析.
- 该方法通过解决多队列研究中缺失和不匹配的措施来最大限度地利用数据.
- 罗塞塔 (ROSETTA) 促进了对不同人群中受社会经济因素影响的复杂健康结果的强有力的分析.
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