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Application of Bayesian approaches in drug development: starting a virtuous cycle

Nature Reviews Drug Discovery volume 22, pages 235–250 (2023)Cite this article

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Abstract

The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens. Nevertheless, despite advances in Bayesian methodology, the availability of the necessary computational power and growing amounts of relevant existing data that could be used, Bayesian methods remain underused in the clinical development and regulatory review of new therapies. Here, we highlight the value of Bayesian methods in drug development, discuss barriers to their application and recommend approaches to address them. Our aim is to engage stakeholders in the process of considering when the use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an effective tool for doing so.

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Fig. 1: Comparison between Bayesian and frequentist approaches.
Fig. 2: Prior distributions in Bayesian clinical trials.
Fig. 3: Posterior distributions in Bayesian clinical trials.
Fig. 4: Recommended stepwise process for deciding whether a frequentist or Bayesian approach is most applicable for design, analysis and interpretation for the test of an experimental hypothesis.

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Acknowledgements

The authors graciously acknowledge the insightful comments of four reviewers and the editor that led to valuable refinements and enhancements to this manuscript.

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Authors and Affiliations

  1. Analytix Thinking, Indianapolis, IN, USA

    Stephen J. Ruberg

  2. Merck KGaA, Darmstadt, Germany

    Francois Beckers

  3. Consilium Salmonson and Hemmings, Woking, UK

    Rob Hemmings

  4. Independent Advisor, Collegeville, PA, USA

    Peter Honig

  5. Janssen Pharmaceutical Companies of J & J, Titusville, NJ, USA

    Telba Irony

  6. University of North Carolina, Chapel Hill, NC, USA

    Lisa LaVange

  7. Genentech, South San Francisco, CA, USA

    Grazyna Lieberman

  8. Pharmaceutical Research and Manufacturers of America, Washington, DC, USA

    James Mayne & Richard Moscicki

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Correspondence to Stephen J. Ruberg.

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Competing interests

S.J.R. is a consultant to the Pharmaceutical Research and Manufacturers of America (PhRMA) and several pharmaceutical companies. F.B. is employed at Leuven University and at Sanofi, where he is a stockholder. Most of the work on this manuscript was completed while employed at Merck KGaA. R.H. is an independent consultant to the pharmaceutical industry. P.H. is a stockholder in Pfizer and Merck and works as an adviser to Blackstone, which invests in many pharmaceutical companies. T.I. is an employee of Janssen Pharmaceutical Companies of Johnson & Johnson and a stockholder. L.L. is an employee of the University of North Carolina, Chapel Hill and has an Intergovernmental Personnel Act (IPA) Assignment with the FDA. She is an expert statistical consultant with the Center for Drug Evaluation and Research. G.L. is employed by a startup health-care data company, N-Power Medicine, but performed work on this manuscript as an employee of Genentech. She is a Roche shareholder. J.M. is a Pfizer stockholder and is retired but performed most of his work on this manuscript while employed by PhRMA. R.M. is retired but performed most of his work on this manuscript while employed by PhRMA. He is a board director of KSQ corporation, a privately held biotech company.

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FDA Center for Drug Evaluation and Research. Complex Innovative Trial Design Meeting Program: https://www.fda.gov/drugs/development-resources/complex-innovative-trial-design-meeting-program

FDA Center for Drug Evaluation and Research. Impact Story: Using innovative statistical approaches to provide the most reliable treatment outcomes information to patients and clinicians: https://www.fda.gov/drugs/regulatory-science-action/impact-story-using-innovative-statistical-approaches-provide-most-reliable-treatment-outcomes

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Ruberg, S.J., Beckers, F., Hemmings, R. et al. Application of Bayesian approaches in drug development: starting a virtuous cycle. Nat Rev Drug Discov 22, 235–250 (2023). https://doi.org/10.1038/s41573-023-00638-0

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