报告题目：如何开展统计交叉科学的研究

报告人：李启寨（中国科学院数学与系统科学研究院，教授）

报告时间：2022年10月25日 9:30-10:30

报告地点：腾讯会议ID：931-248-247

校内联系人：李天然

报告摘要：Balancing allocation of assigning units to two treatment groups to minimize the allocation differences is important in biomedical research. The complete randomization, rerandomization, and pairwise sequential randomization (PSR) procedures can be employed to balance the allocation. However, the first two do not allow a large number of covariates. In this article, we generalize the PSR procedure and propose a k-resolution sequential randomization (k-RSR) procedure by minimizing the Mahalanobis distance between both groups with equal group size. The proposed method can be used to achieve adequate balance and obtain a reasonable estimate of treatment effect. Compared to PSR, k-RSR is more likely to achieve the optimal value theoretically. Extensive simulation studies are conducted to show the superiorities of k-RSR and applications to the clinical synthetic data and GAW16 data further illustrate the methods.在生物医学研究中，平衡分配单元给两个治疗组以最小化分配差异是很重要的。可以采用完全随机化、重新随机化和成对顺序随机化（PSR）程序来平衡分配。然而，前两个不允许有大量的协变量。在这篇文章中，我们推广了PSR程序，并提出了一个k分辨率顺序随机化（k-RSR）程序，通过最小化两个组之间的马氏距离，使组大小相等。所提出的方法可用于实现充分的平衡，并获得对治疗效果的合理估计。与PSR相比，k-RSR在理论上更可能达到最优值。进行了广泛的仿真研究，以显示k-RSR的优越性，并将其应用于临床合成数据和GAW16数据，进一步说明了这些方法.