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在集群随机试验中处理缺失的结果数据,包括个人和集群水平的退学情况

Analissa Avila¹, Beth A Glenn², Roshan Bastani²

¹Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, California, USA.

Statistics in medicine

|September 17, 2025

概括

集群随机试验 (CRT) 中缺少的数据可以是零星的或系统的. 这项研究发现,特定的多重归算方法表现良好,为CRT中缺少的结果数据提供了强大的灵敏度分析.

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:

缺失的结果数据在集群随机试验 (CRT) 中普遍存在,这对统计推断构成了挑战.
缺失可以呈现为零星 (个人学) 或系统 (集群学),可能由不同的机制驱动.
有效处理两种类型的缺失数据对于可靠的CRT结果至关重要.

研究的目的:

开发和评估处理CRT中零星和系统性缺失结果数据的实用方法.
在不同的缺失数据场景下评估各种多层次多重归算 (MI) 技术的性能.
创建灵敏度分析方法来评估根据遗漏随机 (MAR) 和遗漏不随机 (MNAR) 假设的推断稳定性.

主要方法:

一项模拟研究评估了几个多层次多重归算 (MI) 方法的性能,包括完整的条件规范 (FCS),用于处理缺失的CRT结果数据.
该模拟在多级共变量依赖的缺失假设下检查了性能.
开发了新型灵敏度分析方法,以测试个人和集群退学的不同MAR和MNAR假设下的稳定性.

主要成果:

几种基于FCS的MI方法在解决各种模拟场景中零星和系统的CRT缺失方面表现良好.
发现使用双阶段估计器的FCS方法表现不佳.
开发的灵敏度分析方法,结合图形显示,在不同的缺失数据假设下有效地可视化了强度.

结论:

特定的多层MI技术,特别是某些FCS方法,为CRT中管理复杂的缺失结果数据模式提供了可行的解决方案.
建议的灵敏度分析框架允许研究人员评估潜在的未观察到缺失数据机制 (MNAR) 对研究结果的影响.
这些方法提高了CRT结果的可靠性和可解释性,但缺少大量的结果数据.

关键词:

聚类数据是聚类数据.缺失的数据缺失的数据失踪并不是随机发生的.多重的归算是多重的归算.系统地错过了缺失的信息.

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