Why are There not More Bayesian Clinical Trials? Ability to Interpret Bayesian and Conventional Statistics Among Medical Researchers
Abstract
Objective and background We assessed current understandings in interpretation of Bayesian and traditional statistical results within the clinical researcher (non-statistician) community.Methods Within a 22-question survey, including demographics and experience and comfort levels with Bayesian analyses, we included questions on how to interpret both Bayesian and traditional statistical outputs. We also assessed whether Bayesian or traditional interpretations are considered more useful.Results Among the 323 respondent clinicians, 42.4% and 36.5% chose the correct interpretations of the posterior probability and 95% credible interval, respectively. Only 11.5% of respondents interpreted the p-value correctly and 23.5% interpreted the 95% confidence interval correctly.Conclusions Based on these survey results, we conclude that most of these clinicians face uncertainty when attempting to interpret results from both Bayesian and traditional statistical outputs. When presented with accurate interpretations, clinicians generally conclude that Bayesian results are more useful than conventional ones. We believe there is a need for education of clinicians in statistical interpretation in ways that are customized to this audience.
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Some trials are designed based on bayesian methods.
“Some trials are designed based on bayesian methods.416 417”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [416]
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“At present, this low adoption of Bayesian methods is likely due to a lack of familiarity with Bayesian methods, 20 as well as a lack of willingness to take deviate from frequentist approaches to drug development due to perceived risk.”
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Bayesian probabilistic statements for communicating clinical trial results
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“A related limitation is that in this work we did not prospectively assess the added benefit of using Bayesian statistics for communicating effects as was done e.g., by Bray et al. [11]; this was outside our scope, which was mainly didactical, aiming to motivate the use of the methods.”
Influential
- Authors
- The Medical Outreach Team of the Drug Information Association Bayesian Scientific Working Group, Bray, R., Hartley, A., Wenkert, D., Muehlemann, N., Natanegara, F., Harrell, F. E., Wang, F., & Clark, J.
- Affiliations
- Eli Lilly (United States) (US)
- Thermo Fisher Scientific (United States) (US)
- Cytel (United States) (US)
- United States Food and Drug Administration (US)
- Boehringer Ingelheim (Germany) (DE)
- Corresponding author
- Bray, Ross
- Venue
- Therapeutic Innovation & Regulatory Science
- Published
- 2022-12-10
- Volume
- 57
- Issue
- 3
- Pages
- 426-435
- ISSN
- 2168-4790 (ISSN-L)
- 2168-4804
- OpenAlex
- W4311114763
- Cited by
- 9
- References
- 14
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 2
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Therapeutic Innovation & Regulatory Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- Bayesian analysis, clinical trials, statistical interpretation, p-values, confidence intervals, credible intervals, posterior probability, statistics education
- MeSH terms
- Bayes Theorem, Health Personnel, Humans, Probability, Uncertainty
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