根据ICH E9 (R1) 进行随机对照试验中的累积发病率和治疗效果的推断,并根据ICH E9 (R1) 进行时间到事件结果的推断
Yuhao Deng1, Shasha Han2, Xiao-Hua Zhou3,4
1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Statistics in medicine
|May 19, 2025
概括
本研究介绍了随机对照试验 (RCT) 中随机对照试验 (RCT) 中的时间到事件结果分析的方法,其中重点是因果解释. 它提供了定义,估计和推断复杂临床试验数据中的因果关系的实用方法.
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
- 流行病学 流行病学
背景情况:
- 随机对照试验 (RCT) 的时间对事件分析中的间流事件带来了挑战,作为半竞争或竞争事件.
- 在ICH E9 (R1) 附录中现有的策略对于具有时间到事件结果的因果推理并不容易适用.
- 解决这些事件对于准确解释临床研究中治疗效应至关重要.
研究的目的:
- 提供一个框架来定义,估计和推断RCT中因果关系目标的相互流动事件.
- 适应现有的因果解释策略在时间到事件结果设置中.
- 引入新的统计方法来处理临床试验中的半竞争和竞争事件.
主要方法:
- 为与五种ICH E9 (R1) 策略相对应的因果估计数的数学公式的推导.
- 澄清必要的数据结构,以识别这些因果估计.
- 引入非参数估计方法,包括对因果估计的非对称方差和假设测试.
主要成果:
- 建立了明确的数学定义,用于在间流事件存在时的因果估计.
- 开发和验证了非参数方法来估计和对这些因果估计进行推理.
- 使用来自LEADER试验的数据,展示了拟议方法的实际应用.
结论:
- 提出的方法使得可靠的因果推断时间到事件的结果受影响的间流事件在RCTs.
- 该研究为处理复杂事件数据的生物统计学家和临床研究人员提供了宝贵的工具包.
- 应用到LEADER试验强调了这些方法在现实世界心血管结果研究中的实用性.
相关概念视频
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01:24Kaplan-Meier Approach
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