在现实世界的证据中进行可扩展的混调整:在一项大规模试验模拟研究中对数据适应性和研究人员指定的策略进行基准测试
Andrew R Weckstein1,2, Shirley V Wang1, Richard Wyss1
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02120, United States.
Journal of the American Medical Informatics Association : JAMIA
|December 3, 2025
概括
数据适应算法为真实世界的证据提供可扩展的混调整,平均匹配调查员指定的模型. 结合算法和专家方法的混合方法提供了最可靠的因果推理.
科学领域:
- 现实世界的证据 (RWE) 研究研究.
- 因果推理方法论的因果推理方法.
- 健康数据科学健康数据科学
背景情况:
- 手动调整RWE中的混是一个可扩展的挑战.
- 数据适应 (DA) 算法显示出对高维混调整的承诺.
- 在DA和研究人员指定的 (IS) 方法之间进行系统的比较是有限的.
研究的目的:
- 评估DA策略是否与手动策划的IS模型相比性能.
- 通过基于索赔的仿真来评估各种治疗场景的DA性能.
- 将DA和IS方法与随机对照试验 (RCT) 基准进行比较.
主要方法:
- 从索赔数据库中使用新用户队列模拟15个RCT.
- 使用三个调整策略估计治疗效应:IS,全DA和混合DA.
- 评估与RCT基准的一致性,使用二进制指标和差异差异.
主要成果:
- 与IS相比,结果适应的LASSO在73%的全DA和87%的混合DA仿真中显示出优异的RWE-RCT协议.
- 考虑治疗和结果关联的DA方法表现良好;仅治疗预测模型表现不佳.
- 在模拟试验中,IS和DA策略之间的性能差异各不相同.
结论:
- 顶级DA算法平均与IS模型相匹配,但性能不同.
- 专业知识仍然至关重要,特别是在复杂的治疗中.
- DA算法显示可扩展的混调整和增强IS设计的承诺;混合策略提供可靠性.
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