Yu Qingzhao, Zhu Lin, Zhu Han
School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA.
Pharmaceutical Product Development, LLC, 7551 Metro Center Dr 300, Austin, TX 78744, USA.
Pharm Stat. 2017 Nov;16(6):451-465. doi: 10.1002/pst.1830. Epub 2017 Oct 4.
Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this paper, we consider 2-arm clinical trials. Patients are allocated to the 2 arms with a randomization rate to achieve minimum variance for the test statistic. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size.
贝叶斯序贯和自适应随机化设计因其有潜力减少所需参与者数量并节省资源，在临床试验中越来越受欢迎。我们提出一种具有自适应随机化率的贝叶斯序贯设计，以便更有效地将新招募的患者分配到不同治疗组。在本文中，我们考虑双臂临床试验。患者以随机化率分配到两个治疗组，以实现检验统计量的最小方差。给出了计算所提出设计的最优随机化率、临界值和检验功效的算法。进行敏感性分析以检查通过改变先验分布对设计的影响。应用模拟研究在检验功效和实际样本量方面比较所提出的方法和传统方法。模拟表明，当总样本量固定时，所提出的设计比传统贝叶斯序贯设计能获得更大的检验功效和/或花费更小的实际样本量。最后，我们将所提出的方法应用于一个真实数据集，并在样本量方面将结果与无自适应随机化的贝叶斯序贯设计进行比较。所提出的方法可以进一步减少所需样本量。
