复杂疾病过程的临床试验中的估计值
Richard J Cook1, Jerald F Lawless1
1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
Clinical trials (London, England)
|August 24, 2024
概括
随机临床试验面临的挑战是复杂的疾病过程和相互流动的事件. 这项研究澄清了在现实世界临床环境中准确的因果推断的估计和制定.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 因果推理因果推理
背景情况:
- 随机临床试验 (RCT) 是评估实验性治疗与标准护理的关键.
- 复杂的疾病过程与半竞争事件和间流动事件 (例如,戒断,救援药物,死亡) 复杂的因果推断在RCT.
- 目前关于处理这些事件的指导往往含糊不清,可能不反映现实世界的适用性.
研究的目的:
- 解决复杂疾病过程的临床试验中因果推理的目标估值的制定方面的挑战.
- 澄清用于处理间流事件的推方法中使用的估计值的含义.
- 突出理论潜在结果框架与实际临床试验现实之间的断开.
主要方法:
- 从线性模型框架内的间流事件开始讨论估计和制定.
- 应用多状态建模用于更复杂的病史过程.
- 阐明各种方法对相互流动的事件所暗示的估计值.
主要成果:
- 确定复杂的临床试验场景的当前估计和定义中的模两可.
- 展示了一些对间流事件的推方法如何导致与现实世界的相关性有限的估计结果.
- 突出了潜在结果理论与实际估计和解释之间的差异.
结论:
- 需要更清晰,实际相关的估计和定义,用于临床试验的间流事件.
- 多状态建模为复杂疾病过程中更现实的因果推理提供了一个框架.
- 将理论的因果推断与现实世界的临床试验行为相协调,对于产生强有力的证据至关重要.
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