外部比较研究:四种缺失数据处理方法的性能,通过四种不同的边际估计器进行分层
Gerd Rippin1, Héctor Sanz2, Wilhelmina E Hoogendoorn3
1IQVIA, Unterschweinstiege 2-14, 60549, Frankfurt, Germany. gerd.rippin@iqvia.com.
Drug safety
|August 20, 2025
概括
本研究评估了四种方法来处理外部比较研究 (EC) 中缺少的数据. 在队列内多重归算 (MI) 和未治疗 (ATU) 估计器的平均治疗效果显示了EC研究中最佳的偏差减少.
科学领域:
- 因果推理的原因推理.
- 观察性研究是指观察性研究.
- 缺失的数据分析分析.
背景情况:
- 外部对比 (EC) 研究容易因缺少数据和未测量的混而产生偏见.
- 现有的研究已经量化了这些影响,但需要对缺失的数据处理方法进行更广泛的评估.
- 在了解各种统计方法的表现方面存在知识差距,以解决EC研究中缺少数据的问题.
研究的目的:
- 调查四个不同的缺失数据处理策略的性能.
- 通过四种不同的边际估计器来评估这些策略:ATU,ATE,ATT和ATO.
- 为了明确在EC研究中最小化偏差,使用与缺失数据的倾向性得分权重.
主要方法:
- 通过使用两个现实世界EC案例研究,进行了广泛的模拟研究.
- 评估了四种缺失的数据处理方法:队列内MI,队列间MI,混合MI和共变量遗漏.
- 倾向性得分权重被用作因果推断方法,缺失仅在EC队列中模拟.
主要成果:
- 在队列内多重归算 (MI) 和未治疗 (ATU) 估计器的平均治疗效果在偏差缓解方面表现最好.
- 由于缺失率很高,省略预后因素 (共变量) 的策略产生了最糟糕的结果.
- 在不同的边际估计器和缺失的数据处理技术中,性能有所不同.
结论:
- 该研究澄清了EC研究中边际估计器的不同缺失数据策略的性能.
- 研究结果有助于研究人员选择适当的统计方法,在使用倾向分数权重时最大限度地减少偏差.
- 建议包括考虑偏差估计和纠正步骤,以获得更强大的EC研究结果.
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