在研究中评估和比较共变量平衡指标与时间依赖混的研究
David Adenyo1,2, Jason R Guertin1,2, Bernard Candas1
1Département de médecine sociale et préventive, Université Laval, Québec, Quebec, Canada.
Statistics in medicine
|July 31, 2024
概括
这项研究评估了纵向研究中时间变化的治疗方法的平衡指标,发现Mahalanobis平衡对控制边缘结构模型中混偏差最有效.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 健康 数据科学 数据科学
背景情况:
- 边缘结构模型 (MSM) 对于解决纵向研究中的混偏差以及时间变化的治疗方法至关重要.
- 治疗权重的反向概率 (IPTW) 通常用于估计MSM参数.
- 在权重纵向数据中通过平衡指标评估剩余混是必不可少的,但未得到充分研究.
研究的目的:
- 在纵向研究中扩展和比较时间变化的治疗平衡指标.
- 评估这些指标在控制混偏差方面的表现.
- 展示这些指标在现实世界健康数据中的实际应用.
主要方法:
- 对时间变化的治疗方法的平衡指标的扩展定义,对审查的计算.
- 进行了模拟研究,通过将不平衡与偏差相关联来比较指标性能.
- 将表现最好的指标应用于现实世界的数据集,以分析他类药物暴露.
主要成果:
- 马哈拉诺比斯平衡在检测和控制残留混杂方面表现优异.
- 模拟研究证实了拟议的平衡指标的有效性.
- 该方法在大规模的行政数据上得到了成功的说明.
结论:
- 建议的平衡指标,特别是Mahalanobis平衡,对于评估纵向MSM与时间变化的治疗的混是有效的.
- 这些指标可用于大型复杂的健康数据库.
- 这项工作为提高观察性研究中因果推理可靠性提供了有价值的工具.
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