Rerandomization and optimal matching

Pub Date : 2023-07-03 DOI:10.1002/cjs.11783
John D. Kalbfleisch, Zhenzhen Xu
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引用次数: 1

Abstract

On average, randomization achieves balance in covariate distributions between treatment groups; yet in practice, chance imbalance exists post randomization, which increases the error in estimating treatment effects. This is an important issue, especially in cluster randomized trials, where the experimental units (the clusters) are highly heterogeneous and relatively few in number. To address this, several restricted randomization designs have been proposed to balance on a few covariates of particular interest. More recently, approaches involving rerandomization have been proposed that aim to achieve simultaneous balance on several important prognostic factors. In this article, we comment on some properties of rerandomized designs and propose a new design for comparing two or more treatments. This design combines optimal nonbipartite matching of the subjects together with rerandomization, both aimed at minimizing a measure of distance between elements in blocks to achieve reductions in the mean squared error of estimated treatment effects. Compared with the existing alternatives, the proposed design can substantially reduce the mean squared error of the estimated treatment effect. This enhanced efficiency is evaluated both theoretically and empirically, and robustness properties are also noted. The design is generalized to three or more treatment arms.

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重随机化与最优匹配
平均而言,随机化在治疗组之间实现了协变量分布的平衡;然而,在实践中,随机化后存在机会失衡,这增加了估计治疗效果的误差。这是一个重要的问题,尤其是在集群随机试验中,实验单元(集群)高度异质,数量相对较少。为了解决这一问题,已经提出了几种限制性随机化设计,以平衡一些特别感兴趣的协变量。最近,有人提出了涉及重新随机化的方法,旨在同时平衡几个重要的预后因素。在这篇文章中,我们评论了重新随机化设计的一些性质,并提出了一种新的设计来比较两种或多种处理。该设计将受试者的最佳非二分匹配与重新随机化相结合,两者都旨在最小化块中元素之间的距离,以降低估计治疗效果的均方误差。与现有的替代方案相比,所提出的设计可以大大降低估计治疗效果的均方误差。对这种增强的效率进行了理论和经验评估,并注意到了鲁棒性特性。该设计被推广到三个或更多的治疗臂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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