在集群随机试验中的模型-强大的标准化
Fan Li1,2, Jiaqi Tong1,2, Xi Fang1,2
1Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
Statistics in medicine
|September 19, 2025
概括
这项研究引入了一种分析集群随机试验的强大方法,确保准确的治疗效果估计,即使是模型错误规范或信息集群大小. 该方法为集群平均值和个体平均值治疗效应提供了一致的估计值.
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:
- 通用线性混合模型和通用估计方程是集群随机试验的标准.
- 这些常规方法可以产生模两可的治疗效果估计,模型的错误规范或信息集群大小.
研究的目的:
- 在集群随机试验中提出一个统一的,模型强大的估计和对齐推断方法.
- 为集群平均和个体平均治疗效果开发一致的估计器.
主要方法:
- 一种新的标准化方法,使回归模型的输出与估计值保持一致.
- 引入对边际治疗效应的始终一致的估计器.
- 基于删除的杰克刀差异估计器的探索.
- 开发一个测试信息集群大小.
主要成果:
- 建议的估计器确保对治疗效应的一致推断,无论模型规范的准确性如何.
- 该方法提供了一种可靠的方法来处理信息集群大小.
- 模拟研究证实了在各种场景中提出的估计器的优势.
结论:
- 开发的模型-强大的标准化方法在集群随机试验中提供可靠和一致的治疗效果估计.
- 该MRStdCRT R套件实现了这些新型统计方法的实际应用.
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