Abstract：
In a multi-center randomized controlled trial (RCT) with competitive recruitment, eligible patients are enrolled sequentially by different study centers and are randomized to treatment groups using the chosen randomization method. Given the stochastic nature of the recruitment process, some centers may enroll more patients than others, and in some instances, a center may enroll multiple patients in a row, for example, on a given day. If the study is open-label, the investigators might be able to make intelligent guesses on upcoming treatment assignments in the randomization sequence, even if the trial is centrally randomized and not stratified by center. In this paper, we use enrollment data inspired by a real multi-center RCT to quantify the susceptibility of two restricted randomization procedures, the permuted block design and the big stick design, to selection bias under the convergence strategy of Blackwell and Hodges (1957) applied at the center level. We provide simulation evidence that the expected proportion of correct guesses may be greater than 50% (i.e., an increased risk of selection bias) and depends on the chosen randomization method and the number of study patients recruited by a given center that takes consecutive positions on the central allocation schedule. We propose some strategies for ensuring stronger encryption of the randomization sequence to mitigate the risk of selection bias.
摘要：
在一项具有竞争性招募的多中心随机对照试验（RCT）中，符合条件的患者由不同的研究中心依次纳入,并使用选定的随机化方法随机分为治疗组.鉴于招聘过程的随机性，一些中心可能比其他中心招募更多的患者，在某些情况下,一个中心可以连续招募多名患者，例如,在某一天。如果这项研究是开放标签的，研究人员也许能够在随机序列中对即将到来的治疗分配做出明智的猜测，即使该试验是集中随机的,而不是按中心分层.在本文中,我们使用受真实多中心RCT启发的注册数据来量化两个限制性随机化程序的易感性，置换块设计和大棒设计，在布莱克韦尔和霍奇斯(1957)的收敛策略下，选择偏差应用于中心水平。我们提供了模拟证据，证明正确猜测的预期比例可能大于50%(即，选择偏倚的风险增加),这取决于所选择的随机化方法和在中央分配计划中连续任职的给定中心招募的研究患者数量.我们提出了一些策略来确保对随机化序列进行更强的加密，以减轻选择偏差的风险。
