An Empirical Investigation of Bayesian Clinical Trial Design in Metastatic Breast Cancer



Over the past 20 years, there has been increasing interest in the use of Bayesian statistical methods for the analysis of clinical trials used to support regulatory decisions. Bayesian methods for the analysis of clinical trials are an attractive option when good prior information is available. Yet, in many cases, prior information is scarce and only tentative or proprietary prior information exists. In these situations, it is necessary to use noninformative type or skeptical-type priors.


We undertook a systematic study of Bayesian methods and applied them to 13 randomized clinical trials in metastatic breast cancer submitted to the U.S. Food and Drug Administration for registrational purposes. Across all 13 studies, there were a total of 10,521 patients using 10 experimental agents.


Our results demonstrated that Bayesian analyses with noninformative priors provided similar results to more traditional analyses. We also found that early interim looks at the study results can vary widely based upon the type of prior used. Finally, we found that pre-defined threshold stopping rules need to be relatively strong to prevent trials from stopping very early.


Our results suggest that, when prior information is limited and a noninformative prior is used, there is little numerical difference between Bayesian methods and more traditional analysis methods. Bayesian methods, however, may provide additional summaries of the data that are more easily interpretable than means and confidence intervals.

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This paper reflects the views of the authors and should not be construed to represent FDA’s views or policies.

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Correspondence to Erik Bloomquist.

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This work was supported by the Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD.

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Bloomquist, E., Jin, S., Zhou, J. et al. An Empirical Investigation of Bayesian Clinical Trial Design in Metastatic Breast Cancer. Ther Innov Regul Sci 54, 861–869 (2020).

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  • Bayesian
  • Statistics
  • Regulatory
  • Clinical trials
  • Breast cancer