发表机构
George Washington University（乔治华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究协变量自适应随机化中连续协变量离散化的影响，提出设计阶段离散化可增强稳健性，但已知模型时按模型平衡更高效，并通过模拟和糖尿病数据验证。
AI 中文摘要
协变量自适应随机化（CAR）在临床试验中被广泛用于平衡各治疗组间的预后协变量。在实践中，连续协变量常被离散化为分层，但其后果尚未被清晰理解。本文对离散化在CAR设计过程及后续推断结果中的影响进行了全面研究。我们在离散化和非离散化设置下，建立了不平衡度量与治疗效应估计量的渐近性质。针对何时以及如何进行离散化，给出了实用建议。我们表明，设计中的离散化通常被推荐，因为它增强了对模型误设的稳健性。然而，若真实模型已知，最有效的策略是在设计中根据该模型平衡协变量。理论结果得到了广泛模拟研究和对一项糖尿病试验数据集的实证应用的佐证。综合来看，这些结果阐明了CAR中离散化的收益与损失，并为将离散化影响的学习推广到其他设计及更广领域铺平了道路。
英文摘要
Covariate-adaptive randomization(CAR) is widely implemented in clinical trials to balance prognostic covariates across treatment arms. Continuous covariates are often discretized into strata in practice, yet their consequences are not clearly understood. This paper provides a comprehensive study of the impact of discretization on both the CAR design process and the inferential results thereafter. We establish the asymptotic properties of both imbalance measures and treatment effect estimators under discretized and non-discretized settings. Practical recommendations are given on when and how discretization should be employed. We show that discretization in design is generally recommended, as it enhances robustness against model misspecification. However, if the true model is known, the most efficient strategy is to balance covariates according to that model in the design. The theoretical results are corroborated by extensive simulation studies and an empirical application to a diabetes trial dataset. Together, the results clarify the gains and losses of discretization in CAR and pave the way for learning impact of discretization to other designs and beyond.
