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集群级分析以估计集群随机试验中的风险差异与混个体级共变量:一个模拟研究
Jules Antoine Pereira Macedo1, Bruno Giraudeau1,2,
1Université de Tours, Nantes Université, INSERM, SPHERE U1246, Tours, France.
Statistics in medicine
|December 4, 2025
概括
集群随机试验 (CRT) 分析需要仔细考虑个人层面的混因素. 定向最大概率估计 (TMLE) 提供了无偏见的风险差异估计,特别是在少数集群的情况下,优于其他方法.
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:
- 集群随机试验 (CRT) 容易产生招聘偏见,因此需要针对个体级别的混因素进行调整.
- 对于二元结果的传统集群级分析可能无法充分解决CRT中的混问题.
- 估计CRT中的风险差异需要考虑集群和个人层面因素的方法.
研究的目的:
- 为了比较各种分析方法的性能,以估计双臂并行CRT中的风险差异,与个人级别的混仪进行比较.
- 评估对个人层面的调整与个人层面和集群层面的共同变量的影响.
- 在不同的场景下确定CRT分析的最稳健和最公正的方法,特别是在少量集群的情况下.
主要方法:
- 进行了一项模拟研究,以比较分析方法,包括未调整 (UN),两阶段程序 (TSP),G计算 (GC) 和目标最大概率估计 (TMLE).
- 该模拟的重点是双臂并行CRT设计,具有二进制结果,包含个人级别的混因子和集群级别的共变量.
- 使用偏差,I型错误率,覆盖率和标准错误的相对错误来评估性能.
主要成果:
- 未调整的 (UN) 方法表现出偏差.
- 双阶段程序 (TSP) 方法在存在治疗效果并且每个手臂的集群数量很小时存在偏差.
- G计算 (GC) 和目标最大概率估计 (TMLE) 方法提供了公正的估计;TMLE表现出卓越的性能和稳定性,特别是在少数集群的场景中,避免了与GC看到的融合问题.
结论:
- 建议针对性最大概率估计 (TMLE) 用于分析集群随机试验 (CRT) 用个人级别的混因子,特别是当集群数量很少时.
- 在GC和TMLE中仅使用个人级别的共变量进行调整,通常比对个人和集群级别的共变量进行调整更好地表现.
- TMLE提供了一种可靠和公正的CRT风险差异估计方法,在具有有限集群的具有挑战性的场景中表现优于TSP和GC.
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