在观察时间到事件设置中使用权重来估计治疗效应时,建模和平衡方法的性能
Guilherme W F Barros1, Marie Eriksson1, Jenny Häggström1
1Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden.
PloS one
|December 7, 2023
概括
均衡方法改善了在观察性研究中估计危险比率的共变量平衡. 然而,采用变量选择的建模方法在具有挑战性的场景中可能会优于平衡方法,其中重叠差或模型错误规范.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 观察性研究 观察性研究
背景情况:
- 在观察性研究中,权衡技术至关重要,以减轻混偏差.
- 传统的建模方法,如反向倾向得分权重,需要正确的参数模型规范,并且不优先考虑共变量平衡或稳定性.
- 均衡方法已经出现,直接针对共变异不平衡,并允许明确的约束设置.
研究的目的:
- 评估各种建模和平衡方法的有限样本属性,用于估计边际危险比率.
- 在不同的模拟场景下比较这些方法的性能,包括不同程度的重叠和模型错误规范.
- 为了说明这些方法的应用,使用来自瑞典中风登记册的真实世界数据.
主要方法:
- 使用蒙特卡洛模拟来评估不同统计方法的有限样本特性.
- 该研究将平衡方法与传统建模方法进行比较,包括反向倾向得分权重.
- 分析了来自瑞典中风注册的现实数据集,以估计口服抗凝剂对心房患者中风复发或死亡的影响.
主要成果:
- 在具有良好的数据重叠和最小模型错误规范的模拟中,平衡方法与建模方法相似地执行.
- 当面对糟糕的数据重叠和模型错误规范时,采用包含变量选择的建模方法表现出卓越的性能.
- 该研究强调,虽然准共变量平衡是有价值的,但它并不能在所有条件下普遍确保最佳性能.
结论:
- 针对共变量平衡的方法对于在观察性研究中估计边际危险比率是有益的.
- 在重叠不良的场景,高度审查或错误指定的模型/约束的情况下,平衡方法的性能可能会受到损害.
- 在选择方法时,应考虑数据的特定特征和模型错误规格的可能性.
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