在集群随机试验中估计幸存者的平均因果效应的混合模型方法
Wei Wang1, Guangyu Tong2,3,4, Shashivadan P Hirani5
1Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Statistics in medicine
|November 21, 2023
概括
在缺乏生活质量数据的情况下估计治疗效果是具有挑战性的. 这项研究引入了集群随机试验的新统计方法,以解决信息缺失问题,改进因果效应估计.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 在医学研究中对生活质量 (QOL) 结果的信息审查是一个重大的统计挑战.
- 现有的处理缺少QOL数据的方法,如复合结果或归算,依赖于无法测试的假设.
- 幸存者平均因果效应 (SACE) 提供了另一种估计,但对其在集群随机试验中的估计方法有限.
研究的目的:
- 在集群随机试验中开发和评估用于估计SACE的统计方法.
- 解决在集群研究设计中的QOL结果信息缺失处理方法的差距.
- 为因果推理提供一个强大的方法,当结果受到信息丢失的影响时.
主要方法:
- 建议采用混合效应模型方法来估计SACE,考虑集群内相关性.
- 预期最大化算法用于混合效应框架内的参数估计.
- 拟议的方法模拟了主要层的成员,有或没有随机拦截,以捕获复杂的数据结构.
主要成果:
- 模拟证明了与固定效应方法相比,拟议的混合效应方法的性能.
- 结果强调了在集群随机试验中考虑集群内相关性的重要性,以准确SACE估计.
- 该方法得到了验证,使用现实世界的集群随机试验来评估远程医疗干预措施.
结论:
- 开发的混合效应模型为估计集群随机试验中的SACE提供了统计学上合理的方法,这些试验具有信息性缺失数据.
- 这种方法通过适当处理聚类数据结构和信息审查,对现有方法进行了改进.
- 这些发现对设计和分析评估干预措施的临床试验有影响,特别是那些测量与健康相关的QOL的干预措施.
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