网络和共变量调整响应适应式设计用于二进制响应
Hao Mei1,2, Jiaxin Xie2, Yichen Qin3
1Center for Applied Statistics, Renmin University of China, Beijing, China.
Statistics in medicine
|September 26, 2023
概括
新的网络和共变量调整响应适应 (NCARA) 设计提高了临床试验的效率,通过将更多的患者分配给优质治疗,同时平衡社会网络和共变量. 这种适应性随机化方法提高了试验质量和患者益处.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 随机化对于公正的临床试验疗效评估至关重要.
- 现有方法在灵活性,效率和管理网络干扰和共变量方面面临挑战.
- 适应性设计为更动态的试验管理提供了潜在的解决方案.
研究的目的:
- 为了引入网络和协变调整响应适应性 (NCARA) 设计.
- 解决最大限度地提高患者利益,平衡社会网络,同时确保共变量平衡的挑战.
- 评估NCARA设计的性能与其他随机化方法相比.
主要方法:
- 拟议的NCARA设计包括响应适应性随机化与网络和共变量调整.
- 模拟使用各种网络结构和参数设置进行.
- 来自两个临床试验的现实数据被分析,以实施和验证NCARA设计.
主要成果:
- 在模拟中,NCARA设计的表现优于四个替代随机化设计.
- 它证明了可比的统计能力和治疗效应检测的I型错误率.
- 真实数据分析显示,与等随机化相比,功率增加,对优质治疗的分配更高,以及网络/共变量平衡的改善.
结论:
- NCARA设计提供了一种高质量和高效的临床试验管理方法.
- 它的优势在具有小样本大小和高网络干扰的试验中尤其显著.
- NCARA的设计有效地平衡了处理分配,网络联系和共变量分布.
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