When the number of baseline covariates whose imbalance needs to be controlled in a sequential randomized controlled trial is large, minimization is the most commonly used method for randomizing treatment assignments. The lack of allocation randomness associated with the minimization method has been the source of controversy, and the need to reduce even minor imbalances inherent in the minimization method has been challenged. The minimal sufficient balance (MSB) method is an alternative to the minimization method. It prevents serious imbalance from a large number of covariates while maintaining a high level of allocation randomness. In this study, the two treatment allocation methods are compared with regards to the effectiveness of balancing covariates across treatment arms and allocation randomness in equal allocation clinical trials. The MSB method proves to be equal or superior in both respects. In addition, type I error rate is preserved in analyses for both balancing methods, when using a binary endpoint.

中文翻译：

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最小充分平衡和最小化的统计特性作为在序贯临床试验设计阶段控制基线协变量不平衡的方法。

当需要在序贯随机对照试验中控制不平衡的基线协变量数量很大时，最小化是最常用的随机化治疗分配方法。与最小化方法相关的分配随机性的缺乏一直是争议的根源，并且减少最小化方法中固有的即使是微小的不平衡的需求也受到了挑战。最小足够余额 (MSB) 方法是最小化方法的替代方法。它可以防止大量协变量造成的严重不平衡，同时保持高水平的分配随机性。在本研究中，比较了两种治疗分配方法在均衡分配临床试验中平衡治疗组间协变量的有效性和分配随机性。事实证明，MSB 方法在这两个方面是相同或优越的。此外，在使用二进制端点时，两种平衡方法的分析中都会保留 I 类错误率。