Covariate-adjusted randomization (CAR) can reduce the risk of covariate imbalance and, when accounted for in analysis, increase the power of a trial. Despite CAR advances, stratified randomization remains the most common CAR method. Matched Randomization (MR) randomizes treatment assignment within optimally identified matched pairs based on covariates and a distance matrix. When participants enroll sequentially, Sequentially Matched Randomization (SMR) randomizes within matches found "on-the-fly" to meet a pre-specified matching threshold. However, pre-specifying the ideal threshold can be challenging and SMR yields less-optimal matches than MR. We extend SMR to allow multiple participants to be randomized simultaneously, to allow matches to break and rematch if a better match later enrolls (Sequential Rematched Randomization; SRR), and to use a dynamic threshold. In simplified settings and a real-world application, we assess whether these extensions improve covariate balance, estimator/study efficiency, and optimality of matches. We investigate whether adjusting for more covariates can be detrimental upon covariate balance and efficiency as is the case of traditional stratified randomization. As secondary objectives, we use the case study to assess how SMR schemes compare side-by-side with common and related CAR schemes and whether adjusting for covariates in the design can be as powerful as adjusting for covariates in a parametric model. We find each SMR extension, individually and collectively, to improve covariate balance, estimator efficiency, study power, and quality of matches. We provide a case-study where CAR schemes with randomization-based inference can be as and more powerful than Non-CAR schemes with parametric adjustment for covariates.

翻译：协变量调整随机化能降低协变量不平衡风险，并在分析中纳入调整时提高试验效能。尽管协变量调整随机化技术有所进步，分层随机化仍是最常用的方法。匹配随机化基于协变量和距离矩阵，在最优识别匹配对内进行治疗分配随机化。当受试者序贯入组时，序贯匹配随机化通过“实时”找到满足预设匹配阈值的匹配对进行随机化。然而，预设理想阈值具有挑战性，且序贯匹配随机化生成的匹配对质量低于匹配随机化。我们扩展了序贯匹配随机化，使其能够同时随机化多名受试者、允许在后续入组更优匹配时解除原匹配并重新匹配（即序贯再匹配随机化），并采用动态阈值。通过简化场景和实际应用，我们评估了这些改进是否提升了协变量平衡性、估计量/试验效率及匹配最优性。我们检验了调整更多协变量是否会对协变量平衡性和效率产生负面影响，正如传统分层随机化中出现的情况。作为次要目标，我们通过案例研究比较了序贯匹配随机化方案与常见及相关协变量调整随机化方案的并列表现，并探讨在设计中调整协变量是否能够达到与参数模型中调整协变量相同的效果。我们发现，序贯匹配随机化的各项改进（单独及联合使用）均能提升协变量平衡性、估计量效率、试验效能及匹配质量。我们通过案例研究表明，基于随机化推断的协变量调整随机化方案在效能上可等同于甚至优于采用参数协变量调整的非协变量调整随机化方案。