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# Clinicians Need Refresher on Clinical Trial Statistics: Survey
- URL: https://www.fdaweb.com/clinicians-need-refresher-on-clinical-trial-statistics-survey/
- Published: 2022-12-13T12:00:00.000Z
- Updated: 2026-09-14T18:09:39.000Z
- Author: David McFarland
- Tags: FDA Policy/General, #legacy-id-D5153384

A just-published survey has found that clinicians are challenged to interpret results from both Bayesian and traditional statistical outputs from clinical trials. Writing in a 12/10 [*Therapeutic Innovation & Regulatory Science*](https://link.springer.com/journal/43441?ref=fdaweb.com) article, the Drug Information Association’s Bayesian Scientific Working Group, of which FDA is a member, concludes that “there is a need for education of clinicians in statistical interpretation in ways that are customized to this audience.”

“Among the 323 respondent clinicians, 42.4% and 36.5% chose the correct interpretations of the posterior probability and 95% credible interval, respectively,” the authors write. “Only 11.5% of respondents interpreted the p-value correctly and 23.5% interpreted the 95% confidence interval correctly.”

[FDA defines](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-use-bayesian-statistics-medical-device-clinical-trials?ref=fdaweb.com) Bayesian statistics as “an approach for learning from evidence as it accumulates. In clinical trials, traditional (frequentist) statistical methods may use information from previous studies only at the design stage. Then, at the data analysis stage, the information from these studies is considered as a complement to, but not part of, the formal analysis. In contrast, the Bayesian approach uses Bayes’ Theorem to formally combine prior information with current information on a quantity of interest. The Bayesian idea is to consider the prior information and the trial results as part of a continual data stream, in which inferences are being updated each time new data become available.”