Cytel, Cambridge, MA, USA.
Department of Statistics, Colorado State University, Fort Collins, CO, USA.
Ther Innov Regul Sci. 2023 May;57(3):402-416. doi: 10.1007/s43441-023-00515-3. Epub 2023 Apr 20.
Clinical trials continue to be the gold standard for evaluating new medical technologies. New advancements in modern computation power have led to increasing interest in Bayesian methods. Despite the multiple benefits of Bayesian approaches, application to clinical trials has been limited. Based on insights from the survey of clinical researchers in drug development conducted by the Drug Information Association Bayesian Scientific Working Group (DIA BSWG), insufficient knowledge of Bayesian approaches was ranked as the most important perceived barrier to implementing Bayesian methods. Results of the same survey indicate that clinical researchers may find the interpretation of results from a Bayesian analysis to be more useful than conventional interpretations. In this article, we illustrate key concepts tied to Bayesian methods, starting with familiar concepts widely used in clinical practice before advancing in complexity, and use practical illustrations from clinical development.
临床试验仍然是评估新医疗技术的金标准。现代计算能力的新进展使得人们对贝叶斯方法越来越感兴趣。尽管贝叶斯方法有多种好处,但在临床试验中的应用一直受到限制。基于药物信息协会贝叶斯科学工作组(DIA BSWG)对药物开发临床研究人员进行的调查的见解,对贝叶斯方法的了解不足被评为实施贝叶斯方法的最重要的感知障碍。同一调查的结果表明,临床研究人员可能会发现贝叶斯分析结果的解释比传统解释更有用。在本文中,我们将举例说明与贝叶斯方法相关的关键概念,从在临床实践中广泛使用的熟悉概念开始,然后逐步复杂化,并从临床开发中使用实际示例。