在集群随机试验中,在缺少效果修饰器数据的情况下评估治疗效果异质性
Bryan S Blette1, Scott D Halpern2,3, Fan Li4,5
1Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
Statistical methods in medical research
|April 3, 2024
概括
在集群随机试验中处理缺失的数据对于准确的子组分析至关重要. 贝叶斯的多层次多重归算提供了卓越的性能,减少了偏差,并改善了对异质治疗效应的覆盖.
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:
- 评估异质治疗效应 (HTE) 对个性化医学和临床指南至关重要.
- 在随机试验中,对效果修饰剂缺乏数据给HTE评估带来了挑战.
- 缺失数据的现有方法主要是为个人随机试验开发的,对集群随机试验 (CRT) 的指导有限.
研究的目的:
- 为了比较各种缺失数据方法的性能,以评估CRT中的HTE与缺失的二进制效应修饰器.
- 在连续结果和潜在的集群内相关性背景下评估方法.
主要方法:
- 使用集群随机化试验设计进行了模拟研究,该试验设计具有连续结果和缺失的二进制效应修饰器.
- 他们比较了处理缺失数据的几种统计方法,包括多层次多重归算 (MMI) 和贝叶斯多层次多重归算 (BMMI).
- 这些方法进一步用工作,家庭和健康研究中的真实世界数据来说明.
主要成果:
- 与其他评估方法相比,多级多次归算和贝叶斯多级多次归算表现出优异的性能.
- 比起标准的多层次多重归算,贝叶斯的多层次多重归算表现出较低的偏差,并且实现了比标准的多层次多重归算更接近名义水平的覆盖范围,特别是当模型规范或兼容性问题出现时.
- 效果修饰剂,结果或缺失机制中的集群内相关性可能会威胁到CRT中HTE的准确评估.
结论:
- 贝叶斯的多层次多重归算是处理在集群随机试验中缺失的效果修饰器数据的强有力的方法.
- 建议采用这种方法,以便在复杂的试验环境中准确和可靠地评估异质治疗效应.
- 这些发现为设计和分析缺少数据的集群随机试验的统计学家和研究人员提供了必要的指导.
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