Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Pfizer Inc., New York, NY, USA.
Orphanet J Rare Dis. 2022 May 7;17(1):186. doi: 10.1186/s13023-022-02342-5.
Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.
Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.
The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.
罕见病和超罕见病临床试验的设计和分析对从业者提出了独特的挑战。对于样本量本身非常小的疾病，满足常规功效要求是不可行的。此外，罕见病人群通常具有异质性且分布广泛，这使得研究入组和设计变得复杂。在罕见病和超罕见病试验中利用所有可用信息可以提高药物开发和决策过程的信息质量。
贝叶斯统计学为在临床试验的所有阶段（包括试验设计、执行和分析）结合所有相关信息提供了一个正式框架。本文概述了适用于罕见病临床试验的不同贝叶斯方法。我们展示了真实或假设的案例研究，这些案例研究解决了罕见病药物开发的关键需求，强调了临床试验的几个具体贝叶斯示例。详细讨论了这些方法的优缺点。此外，我们还强调了实际应用中的实际和监管方面。
在罕见病环境中，结合贝叶斯方法使用创新的试验设计，如主方案和复杂适应性设计，可以帮助减少样本量、选择正确的治疗方法和人群，并准确可靠地评估治疗效果。
