集群随机2x2因子试验中的共变量受约束随机化:适用于糖尿病预防研究
Juned Siddique1, Zhehui Li1, Matthew J O'Brien2
1Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N Lake Shore Drive, Suite 1400, Chicago, IL, USA.
Research square
|April 8, 2024
概括
同变量受约束的随机化 (CR) 改善了因子集群随机化试验 (CRT) 的平衡. CR提供了更精确的推断,并且应该使用它而不是简单的随机化,以获得更可靠的结果.
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
- 公共卫生研究 公共卫生研究
背景情况:
- 集群随机试验 (CRT) 涉及组级随机化,在小集群数量的情况下,对共变量平衡提出挑战.
- 集群级共变量的不平衡可能会导致治疗效应估计的偏差,降低统计能力,并增加CRT的结果变化.
- 共变量受约束的随机化 (CR) 是一种策略,用于减轻CRT中的共变量失衡,使用以前未开发的因数设计的方法.
结论:
- 建议对因数式CRT进行共变量受约束的随机化 (CR),而不是简单的随机化,以防止不平衡的设计并提高推断精度.
- 分析模型应在因数CRT中纳入集群级共变量,除非处理最小数量的集群,以优化功率和统计有效性.
- 这些发现支持采用CRT,以在复杂的因数CRT设计中获得更强大,更可靠的结果.
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