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对于部分观察到的混因子,包括自然语言处理衍生的辅助共变量,进行高维多重计量 (HDMI)
Janick Weberpals1, Pamela A Shaw2, Kueiyu Joshua Lin1
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
使用辅助共变量 (AC) 的高维多重归算 (MI) 可以减少缺少混因子的研究中的偏差. 结合结构化和NLP衍生的AC,在模拟中提高了效率和偏差.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 辅助共变量 (AC) 可以增强多重归算 (MI) 模型.
- 在高维数据中AC的性能,特别是与部分观察到的混因子,需要进一步调查.
- 自然语言处理 (NLP) 提供了从非结构化数据中导出AC的新方法.
研究的目的:
- 开发和比较使用结构化和NLP衍生AC的高维MI (HDMI) 方法.
- 在模拟队列中评估HDMI性能,部分观察到混因子,特别关注急性损伤研究.
- 评估遗漏关键混因子 (如心房) 对归算和治疗效果估计的影响.
主要方法:
- 用100个队列进行了等离子模拟,模拟了急性损伤结果和零治疗效应.
- 氨酸实验室值和心房动 (AFib) 被列入混因子,缺失被强加于氨酸.
- 高维MI (HDMI) 协变量来自结构化 (索赔) 和NLP衍生的特征,使用LASSO选择MI和倾向得分模型.
主要成果:
- 使用HDMI索赔数据的数据显示偏差最小 (0.072).
- 将索赔数据与NLP衍生的句子嵌入相结合,提高了效率 (RMSE=0.173),并实现了94%的覆盖率.
- 单独NLP衍生的AC并没有超过基线MI方法.
- 完整的病例分析和基线归算显示,与HDMI方法相比,偏差更高.
结论:
- 用辅助共变量进行高维多重归算 (HDMI) 可以有效地减少与未观察到因素相关的混缺失的研究中的偏差.
- 整合结构化数据 (索赔) 和NLP衍生特征,为提高复杂,高维数据集中归算的准确性和效率提供了一个有希望的方法.
- 仔细选择和导出辅助共变量对于HDMI方法在观察性健康研究中的成功应用至关重要.
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