Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
Cytel, Cambridge, MA, USA.
Ther Innov Regul Sci. 2023 May;57(3):417-425. doi: 10.1007/s43441-021-00357-x. Epub 2022 Jan 3.
The clinical trials community has been hesitant to adopt Bayesian statistical methods, which are often more flexible and efficient with more naturally interpretable results than frequentist methods. We aimed to identify self-reported barriers to implementing Bayesian methods and preferences for becoming comfortable with them.
We developed a 22-question survey submitted to medical researchers (non-statisticians) from industry, academia, and regulatory agencies. Question areas included demographics, experience, comfort levels with Bayesian analyses, perceived barriers to these analyses, and preferences for increasing familiarity with Bayesian methods.
Of the 323 respondents, most were affiliated with pharmaceutical companies (33.4%), clinical research organizations (29.7%), and regulatory agencies (18.6%). The rest represented academia, medical practice, or other. Over 56% of respondents expressed little to no comfort in interpreting Bayesian analyses. "Insufficient knowledge of Bayesian approaches" was ranked the most important perceived barrier to implementing Bayesian methods by a plurality (48%). Of the approaches listed, in-person training was the most preferred for gaining comfort with Bayesian methods.
Based on these survey results, we recommend that introductory level training on Bayesian statistics be presented in an in-person workshop that could also be broadcast online with live Q&A. Other approaches such as online training or collaborative projects may be better suited for higher-level trainings where instructors may assume a baseline understanding of Bayesian statistics. Increased coverage of Bayesian methods at medical conferences and medical school trainings would help improve comfort and overcome the substantial knowledge barriers medical researchers face when implementing these methods.
临床试验界一直对采用贝叶斯统计方法犹豫不决，与频繁主义方法相比，贝叶斯方法通常更灵活、更有效，且结果更易于解释。我们旨在确定实施贝叶斯方法的自我报告障碍以及对其感到舒适的偏好。
我们开发了一个 22 个问题的调查，提交给来自行业、学术界和监管机构的医学研究人员（非统计学家）。问题领域包括人口统计学、经验、对贝叶斯分析的舒适度、对这些分析的感知障碍以及对增加对贝叶斯方法熟悉程度的偏好。
在 323 名受访者中，大多数人隶属于制药公司（33.4%）、临床研究组织（29.7%）和监管机构（18.6%）。其余的代表学术界、医疗实践或其他领域。超过 56%的受访者表示对解释贝叶斯分析几乎没有舒适度。“对贝叶斯方法的知识不足”被列为实施贝叶斯方法的最重要感知障碍，占比为 48%。在所列出的方法中，面对面培训是获得贝叶斯方法舒适度的最受欢迎的方法。
根据这些调查结果，我们建议在面对面的研讨会上介绍贝叶斯统计学的入门级培训，也可以通过在线直播和实时问答来进行。其他方法，如在线培训或合作项目，可能更适合高级培训，因为在这些培训中，讲师可能假设学员对贝叶斯统计学有基本的了解。在医学会议和医学院培训中增加对贝叶斯方法的覆盖，将有助于提高舒适度并克服医学研究人员在实施这些方法时面临的巨大知识障碍。
