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

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Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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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References

  1. U.S. Food and Drug Administration, Center for Devices and Radiological Health. Guidance for the use of Bayesian statistics in medical device clinical trials. 2010. https://www.fda.gov/MedicalDevices/ucm071072.htm.

  2. Berry DA. Adaptive clinical trials in oncology. Nat Rev Clin Oncol. 2011;9(4):199–207.

    Article  Google Scholar 

  3. Wandel S, Neuenschwander B, Röver C, Friede T. Using phase II data for the analysis of phase III studies: an application in rare diseases. Clin Trials. 2017;14(3):277–85.

    Article  Google Scholar 

  4. Iasonos A, O’Quigley J. Early phase clinical trials-are dose expansion cohorts needed? Nat Rev Clin Oncol. 2015;12(11):626–8.

    Article  CAS  Google Scholar 

  5. Infante JR, Cassier PA, Gerecitano JF, et al. A phase I study of the cyclin-dependent kinase 4/6 inhibitor ribociclib (LEE011) in patients with advanced solid tumors and lymphomas. Clin Cancer Res. 2016;22(23):5696–705.

    Article  CAS  Google Scholar 

  6. Kesselheim AS, Mello MM. Confidentiality laws and secrecy in medical research: improving public access to data on drug safety. Health Aff. 2007;26(2):483–91.

    Article  Google Scholar 

  7. Efron B, Hastie T. Computer age statistical inference: algorithms, evidence, and data science. New York: Cambridge University Press; 2016.

    Book  Google Scholar 

  8. Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian approaches to clinical trials and health-care evaluation. Chichester: Wiley; 2004.

    Google Scholar 

  9. Wedam S, Beaver JA, Amiri-Kordestani, et al. US Food and Drug Administration pooled analysis to assess the impact of bone-only metastatic breast cancer on clinical trial outcomes and radiographic assessments. J Clin Oncol. 2018;36(12):1225–31.

    Article  CAS  Google Scholar 

  10. Allison PD. Survival analysis using SAS: a practical guide. 2nd ed. Cary: SAS Institute; 2010.

    Google Scholar 

Download references

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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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Authors and Affiliations

  1. Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA

    Erik Bloomquist, Susan Jin, Jiaxi Zhou, Shenghui Tang & Rajeshwari Sridhara

  2. Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Building 21, Room 3542, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA

    Erik Bloomquist

Authors
  1. Erik Bloomquist
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  2. Susan Jin
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  3. Jiaxi Zhou
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  4. Shenghui Tang
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  5. Rajeshwari Sridhara
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Correspondence to Erik Bloomquist.

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The authors declare that there are no conflicts of interest.

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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). https://doi.org/10.1007/s43441-019-00002-8

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  • DOI: https://doi.org/10.1007/s43441-019-00002-8

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