最小化的一种应用,以确保在群体随机化COVID-19教育干预试验中确保平衡的研究臂
Xu Zhang1,2, Mohammad H Rahbar1,2, Amirali Tahanan1
1Biostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX, United States.
Contemporary clinical trials communications
|February 12, 2025
概括
一种新的最小化方法在COVID-19干预试验中成功地平衡了研究组. 这种技术确保了健康差异和人口规模在不同群体之间的公平分配,改善了健康公平研究的试验设计.
科学领域:
- 公共卫生 公共卫生
- 生物统计学 生物统计学
- 健康差距 研究 研究 研究 研究
背景情况:
- 多中心,群体随机试验评估了德克萨斯州高差距地区的COVID-19测试和疫苗接种干预措施.
- 具有高差异的人口普查区组 (CBG) 被选择用于随机化.
- 确保连续共变量 (不平等指数,人口规模) 在研究组之间均衡分布是关键目标.
研究的目的:
- 描述和评估一种新型的最小化方法,用于在群组随机试验中平衡连续共变量.
- 在健康差异的背景下,确保干预和控制组之间的公平代表性.
主要方法:
- 使用最大的双向曼哈顿距离作为失衡得分的最小化方法被采用.
- 共变量 (不平等指数,人口规模) 通过基线平均值和标准偏差进行标准化.
- 随机化涉及计算所有可能的分配的不平衡得分,并使用不平等的分配概率.
主要成果:
- 在模拟和试验结果中,最小化方法成功地平衡了差异指数和人口大小的边际分布.
- 实际试验中的研究组在差异指数和人口规模的联合分布方面高度均 (p=0.91).
结论:
- 双向曼哈顿距离的最大值是最小化的实用和有效的不平衡得分.
- 描述的最小化程序令人满意地平衡了组随机试验中的连续共变量分布,特别是在健康差异研究中.
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