Department of Statistics, George Washington University, Washington, DC, USA.
Institute of Statistics and Big Data, Renmin University of China, Beijing, China.
Stat Med. 2021 May 30;40(12):2839-2858. doi: 10.1002/sim.8939. Epub 2021 Mar 17.
Covariate-adaptive randomization (CAR) procedures have been developed in clinical trials to mitigate the imbalance of treatments among covariates. In recent years, an increasing number of trials have started to use CAR for the advantages in statistical efficiency and enhancing credibility. At the same time, sample size re-estimation (SSR) has become a common technique in industry to reduce time and cost while maintaining a good probability of success. Despite the widespread popularity of combining CAR designs with SSR, few researchers have investigated this combination theoretically. More importantly, the existing statistical inference must be adjusted to protect the desired type I error rate when a model that omits some covariates is used. In this article, we give a framework for the application of SSR in CAR trials and study the underlying theoretical properties. We give the adjusted test statistic and derive the sample size calculation formula under the CAR setting. We can tackle the difficulties caused by the adaptive features in CAR and prove the asymptotic independence between stages. Numerical studies are conducted under multiple parameter settings and scenarios that are commonly encountered in practice. The results show that all advantages of CAR and SSR can be preserved and further improved in terms of power and sample size.
协变量自适应随机化（CAR）方法已在临床试验中得到发展，以减轻协变量之间治疗分配的不平衡。近年来，越来越多的试验开始使用 CAR 方法，以提高统计效率和增强可信度。同时，样本量重新估计（SSR）已成为工业界常用的技术，以在保持高成功率的同时减少时间和成本。尽管将 CAR 设计与 SSR 相结合的方法得到了广泛应用，但很少有研究人员从理论上对此进行研究。更重要的是，当使用忽略某些协变量的模型时，必须调整现有的统计推断，以保护期望的Ⅰ类错误率。本文给出了 SSR 在 CAR 试验中的应用框架，并研究了其潜在的理论性质。我们给出了在 CAR 环境下调整后的检验统计量，并推导出了计算公式。我们可以解决 CAR 中自适应特征带来的困难，并证明阶段之间的渐近独立性。在常见的实际参数设置和场景下进行了数值研究。结果表明，在功效和样本量方面，CAR 和 SSR 的所有优势都可以得到保留并进一步提高。
