Why are not There More Bayesian Clinical Trials? Perceived Barriers and Educational Preferences Among Medical Researchers Involved in Drug Development.

Objective And Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.