历史受限的边缘结构模型和治疗轨迹的潜在类增长分析,以获得时间依赖的结果
Awa Diop1, Caroline Sirois2, Jason R Guertin3
1Département de médecine sociale et préventive, Université Laval, Centre de recherche du CHU de Québec - Université Laval, Axe santé des populations et pratiques optimales en santé, Québec, QC, Canada.
The international journal of biostatistics
|August 13, 2024
概括
本研究引入了一种新的因果推理方法,将潜伏阶级增长分析与历史限制的边缘结构模型相结合,以获得时间依赖的结果. 这种新的方法发现,较高的他类药物坚持性降低了老年人心血管疾病的风险.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 因果推理因果推理
背景情况:
- 现有的带有边缘结构模型 (LCGA-MSM) 的潜阶级增长分析不适合时间依赖的结果.
- 时间变化的治疗模式需要先进的因果推断方法来进行准确的分析.
研究的目的:
- 提出和评估一个新的框架,将潜伏类增长分析 (LCGA) 与非参数历史限制边缘结构模型 (HRMSM) 结合起来.
- 在生存分析中解决HRMSM的解释挑战,并引入新的因果参数.
- 在拟议的LCGA-HRMSM框架内评估不同估计器 (IPTW,g计算,聚合LTMLE) 的性能.
主要方法:
- 开发LCGA-HRMSM框架,以实现时间依赖的结果.
- 应用治疗权重的反向概率 (IPTW),g计算和聚合的纵向目标最大概率估计器 (聚合的LTMLE).
- 模拟研究以评估估计器性能和偏差.
- 对57,211名他类药物发起者的现实世界数据集的分析.
主要成果:
- 在模拟中,G计算和聚合的LTMLE提供了公正的估计,而IPTW在某些场景中显示了稍大的偏差.
- 所有测试方法都显示出很好的95%置信区间覆盖率.
- 该LCGA-HRMSM方法已成功应用于一个大队伍的老年人.
结论:
- 拟议的LCGA-HRMSM框架是一种可行的方法,用于分析随时间变化的治疗时间依赖的结果.
- 在老年人中,对他类药物治疗的更强的坚持与心血管疾病或全因死亡率降低的风险有关.
- 这项研究强调了先进的因果推断方法在流行病学研究中的有用性.
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