Wu Jianrong, Li Yimei, Zhu Liang, Patni Tushar
Biostatistics Shared Resource Facility, University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA.
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
J Biopharm Stat. 2024 Sep 16:1-12. doi: 10.1080/10543406.2024.2398036.
Traditional two-arm randomized trial designs have played a pivotal role in establishing the efficacy of medical interventions. However, their efficiency is often compromised when confronted with multiple experimental treatments or limited resources. In response to these challenges, the multi-arm multi-stage designs have emerged, enabling the simultaneous evaluation of multiple treatments within a single trial. In such an approach, if an arm meets efficacy success criteria at an interim stage, the whole trial stops and the arm is selected for further study. However when multiple treatment arms are active, stopping the trial at the moment one arm achieves success diminishes the probability of selecting the best arm. To address this issue, we have developed a group sequential multi-arm multi-stage survival trial design with an arm-specific stopping rule. The proposed method controls the familywise type I error in a strong sense and selects the best promising treatment arm with a high probability.
传统的双臂随机试验设计在确立医学干预措施的疗效方面发挥了关键作用。然而，当面对多种实验性治疗方法或资源有限时，其效率往往会受到影响。为应对这些挑战，多臂多阶段设计应运而生，它能够在单一试验中同时评估多种治疗方法。在这种方法中，如果某一组在中期阶段达到疗效成功标准，整个试验就会停止，该组将被选出来进行进一步研究。然而，当多个治疗组都在进行时，在其中一组取得成功时就停止试验会降低选出最佳组的概率。为解决这一问题，我们开发了一种具有组特异性停止规则的成组序贯多臂多阶段生存试验设计。所提出的方法在严格意义上控制了家族性I型错误，并以高概率选出最有前景的治疗组。
