将主要因果影响的可靠估计乘以不遵守和生存结果的结果
Chao Cheng1, Yueqi Guo2, Bo Liu2
1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Clinical trials (London, England)
|May 30, 2024
概括
这项研究引入了一种强大的方法来评估临床试验中的治疗效果,尽管患者不遵守和审查. 该方法使用主要层来分析对生存的因果影响,提高了现实世界数据分析的可靠性.
科学领域:
- 生物统计学和临床试验方法学
- 在健康研究中的因果推理.
背景情况:
- 治疗不遵守和审查是临床试验中的重大挑战,可能会影响治疗效果评估.
- 实用性临床试验,如ADAPTABLE,通常涉及复杂的现实世界患者行为,需要先进的分析方法.
研究的目的:
- 开发和验证一种多重可靠的统计方法,以估计因果治疗效应在存在正确审查的生存结果和治疗不合规的情况下.
- 在ADAPTABLE试验中,评估低剂量阿司匹林与高剂量阿司匹林对死亡率和心血管疾病住院治疗的因果关系.
主要方法:
- 根据在治疗和控制下潜在的合规状态将参与者分为主要层次的分类.
- 开发一种对生存概率尺度上的因果影响的多重可靠估计器,对最多两种工作模型的错误规格 (治疗分配,主要层次,审查,结果) 进行可靠估计.
- 实施敏感性分析策略,以评估潜在违反主要可忽略性和单调性假设的影响.
主要成果:
- 提出的多重稳定估计器即使在错误指定的工作模型中也表现出一致性,为因果效应估计提供了更高的可靠性.
- 应用到 ADAPTABLE 试验提供了关于不同阿司匹林剂量对死亡率和心血管事件的比较有效性的见解.
结论:
- 开发的统计框架有效地解决了治疗不合规性和生存分析中的审查问题,在实用试验中提供了更可靠的因果效应估计.
- 该方法为寻求了解复杂,现实世界医疗保健环境中的治疗影响的研究人员提供了有价值的工具,以适应性阿司匹林研究为例.
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