Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, 66160, USA.
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA.
Trials. 2022 Sep 6;23(1):754. doi: 10.1186/s13063-022-06664-4.
Platform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit, and are robust to changes over time.
To address these needs, we present a Bayesian platform trial design based on a beta-binomial model for binary outcomes that uses three key strategies: (1) hierarchical modeling of subgroups within treatment arms that allows for borrowing of information across subgroups, (2) utilization of response-adaptive randomization (RAR) schemes that seek a tradeoff between statistical power and patient benefit, and (3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules, and study the model operating characteristics.
Our proposed approach achieved high statistical power and good patient benefit and was also robust against population drift over time. Our design provided a good balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.
平台试验以能够在异质患者人群中研究多个试验组，并能够根据疗效/缺乏疗效灵活添加/删除治疗组而闻名。由于其复杂性，开发高度优化、透明和严格的设计非常重要，这些设计要具有成本效益、提供高统计功效、最大限度地提高患者获益，并能够抵御随时间的变化。
为了满足这些需求，我们提出了一种基于二项式模型的贝叶斯平台试验设计，该设计用于二分类结局，使用了三个关键策略：（1）在治疗组内对亚组进行层次建模，允许跨亚组共享信息；（2）利用响应适应性随机化（RAR）方案，在统计功效和患者获益之间寻求权衡；（3）调整随时间的潜在漂移。受旨在为缺血性脑卒中患者不同亚组人群找到合适治疗方法的拟议临床试验的启发，我们进行了广泛的模拟研究，以验证该方法、比较不同的分配规则，并研究模型的操作特征。
我们提出的方法实现了高统计功效和良好的患者获益，并且对随时间的人群漂移也具有稳健性。我们的设计在传统 RAR 方案和固定 1:1 分配的优势之间取得了良好的平衡，对于研究多个亚组的二分类结局试验可能是一个有前途的选择。
