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 use a dynamic threshold, and to allow matches to break and rematch if a better match later enrolls (Sequential Rematched Randomization; SRR). 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（CAR）可降低协变量不平衡的风险，并在分析中纳入调整时增加试验的效能。尽管CAR有所进展，分层随机化仍是应用最广泛的CAR方法。匹配随机化（MR）基于协变量和距离矩阵，在最优识别的匹配对中进行治疗分配随机化。当参与者序贯入组时，序贯匹配随机化（SMR）在“实时”发现的匹配中按预设匹配阈值进行随机化。然而，预设理想阈值具有挑战性，且SMR生成的匹配质量低于MR。我们对SMR进行扩展，允许同时随机化多个参与者、使用动态阈值，并允许在后期入组更优匹配时打破原有匹配并重新匹配（序贯重匹配随机化；SRR）。在简化场景和实际应用中，我们评估这些扩展能否改善协变量平衡、估计量/研究效率及匹配最优性。我们探究调整更多协变量是否会对协变量平衡和效率产生不利影响（类似于传统分层随机化）。作为次要目标，我们通过案例研究对比SMR方案与常见及相关CAR方案的并列表现，并检验设计阶段调整协变量能否与参数模型中调整协变量具有同等效能。研究发现，每个SMR扩展（单独或组合）均能改善协变量平衡、估计量效率、研究效能及匹配质量。我们通过案例证明，基于随机化推断的CAR方案可与基于协变量参数调整的非CAR方案效能相当甚至更优。