临床试验中的多重归因 - 它有什么不同? :对多重归算对治疗效果估计影响的系统审查
Thomas R Sullivan1,2, Katherine J Lee3,4, Jana M Bednarz1,2
1Women and Kids Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
Clinical trials (London, England)
|March 4, 2026
概括
在临床试验中,多重归算 (MI) 并没有比完整病例分析 (CCA) 始终提高治疗效果的精度. 预计缺失数据的膨胀样本大小仍然是规划MI分析的试验的合理方法.
科学领域:
- 临床试验方法论 临床试验方法论
- 医疗保健中的统计分析.
- 生物统计学 生物统计学
背景情况:
- 对于临床试验中缺少的数据,多重归算 (MI) 是首选的,但它对样本大小规划的影响往往被忽视.
- 如果使用MI,用于完整案例分析 (CCA) 的标准样本大小膨胀可能导致资源分配效率低下.
- 本综述量化了MI与CCA的精度优势,以告知样本大小估计.
研究的目的:
- 系统地审查临床试验并量化多重归算 (MI) 与完整病例分析 (CCA) 的精度优势.
- 提供基于证据的建议,用于临床试验中的样本大小规划,当MI用于分析时.
主要方法:
- 在顶级医学期刊 (2019-2023) 中发表的临床试验的有针对性的审查.
- 纳入标准:试验报告MI和CCA的点和方差估计.
- 对MI与CCA的设计效应 (DE) 的计算.
主要成果:
- 59项试验符合纳入标准;参与者的中位数:653;缺失数据的中位数:8.6%.
- 对MI的设计效应中位数为1.00 (未调整和调整的分析),表明没有比CCA有一致的精度优势.
- 在补充的非目标审查中也发现了类似的结果,即使缺失数据率更高.
结论:
- 多重归算并不总是产生比审查试验中的完整病例分析更精确的治疗效果估计.
- 为了预期后续损失而膨胀样本大小的标准方法即使在计划进行MI分析时也是适当的.
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