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Bayesian Design of Proof-of-Concept Trials

  • Biostatistics
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Abstract

The proof-of-concept (PoC) decision is a key milestone in the clinical development of an experimental treatment. A decision is taken on whether the experimental treatment is further developed (GO), whether its development is stopped (NO-GO), or whether further information is needed to make a decision. The PoC decision is typically based on a PoC clinical trial in patients comparing the experimental treatment with a control treatment. It is important that the PoC trial be designed such that a GO/NO-GO decision can be made. The present work develops a generic, Bayesian framework for defining quantitative PoC criteria, against which the PoC trial results can be assessed. It is argued that PoC criteria based solely on significance testing versus the control are not appropriate in this decision context. A dual PoC criterion is proposed that includes assessment of superiority over the control and relevance of the effect size and hence better matches clinical decision making. The approach is illustrated for 2 PoC trials in cystic fibrosis and psoriasis.

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References

  1. Sheiner LB. Learning versus confirming in clinical drug development. Clin Pharmacol Ther. 1997;61(3):275–291.

    Article  CAS  Google Scholar 

  2. Cartwright ME, Cohen S, Fleishaker JC, et al. Proof of concept: a PhRMA position paper with recommendations for best practice. Clin Pharmacol Ther. 2010;87(3):278–285.

    Article  CAS  Google Scholar 

  3. DiMasi JA, Feldman L, Seckler A, Wilson A. Trends in risks associated with new drug development: success rates for investigational drugs. Clin Pharmacol Ther. 2010;87(3):272–277.

    Article  CAS  Google Scholar 

  4. Mallinckrodt C, Molenberghs G, Persinger C, Ruberg S, Sashegyi A, Lindborg S. A portfolio-based approach to optimize proof-of-concept clinical trials. J Biopharm Stat. 2012;22(3):596–607.

    Article  Google Scholar 

  5. Neuenschwander B, Rouyrre N, Hollaender N, Zuber E, Branson M. A proof of concept phase II non-inferiority criterion. Stat Med. 2011;30(13):1618–1627.

    Article  Google Scholar 

  6. Chuang-Stein C, Kirby S, Hirsch I, Atkinson G. The role of the minimum clinically important difference and its impact on designing a trial. Biopharm Stat. 2011;10(3):250–256.

    Article  Google Scholar 

  7. Chuang-Stein C, Kirby S, French J, et al. A quantitative approach for making go/no-go decisions in drug development. Drug Inf J. 2011;45:187–202.

    Article  Google Scholar 

  8. Spiegelhalter DJ, Freedman LS, Parmar MKB. Applying Bayesian ideas in drug development and clinical trials. Stat Med. 1993;12 (15–16):1501–1511.

    Article  CAS  Google Scholar 

  9. Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials and Health Care Evaluation. New York, NY: John Wiley & Sons; 2004.

    Google Scholar 

  10. Hobbs BP, Carlin BP. Practical Bayesian design and analysis for drug and device clinical trials. J Biopharm Stat. 2007;18:54–80.

    Article  Google Scholar 

  11. Berry SM, Carlin BP, Lee JJ, Müller P. Bayesian Adaptive Methods for Clinical Trials. London, UK: Chapman & Hall/CRC; 2010.

    Book  Google Scholar 

  12. Gelfand AE, Smith AFM. Sampling-based approaches to calculating marginal densities. J Am Stat Assoc. 1990;85:398–409.

    Article  Google Scholar 

  13. Neuenschwander B, Capkun-Niggli G, Branson M, Spiegelhalter DJ. Summarizing historical information on controls in clinical trials. Clin Trials. 2010;7(1):5–18.

    Article  Google Scholar 

  14. Gsteiger S, Neuenschwander B, Mercier F, Schmidli H. Using historical control information for the design and analysis of clinical trials with overdispersed count data. Stat Med. 2013;32(21):3609–3622.

    Article  Google Scholar 

  15. Bansback N, Sizto S, Sun H, Feldman S, Willian MK, Anis A. Efficacy of systemic treatments for moderate to severe plaque psoriasis: systematic review and meta-analysis. Dermatology. 2009;219(3):209–218.

    Article  CAS  Google Scholar 

  16. Kieser M, Hauschke D. Assessment of clinical relevance by considering point estimates and associated confidence intervals. Biopharm Stat. 2005;4(2):101–107.

    Article  Google Scholar 

  17. Smith MK, Jones I, Morris MF, Grieve AP, Tan K. Implementation of a Bayesian adaptive design in a proof of concept study. Biopharm Stat. 2006;5:39–50.

    Article  Google Scholar 

  18. Gsponer T, Gerber F, Bornkamp B, Ohlssen D, Vandemeulebroecke M, Schmidli H. A practical guide to Bayesian group sequential designs. Biopharm Stat. 2014;13(1):71–80.

    Article  Google Scholar 

  19. Gerber F, Gsponer T. gsbDesign: an R Package for evaluating operating characteristics for a group sequential Bayesian design. submitted to J Stat Softw. 2014.

  20. Dmitrienko A, Wang M-D. Bayesian predictive approach to interim monitoring in clinical trials. Stat Med. 2006;25:2178–2195.

    Article  Google Scholar 

  21. Baeten D, Baraliakos X, Braun J, et al. Anti-interleukin-17A monoclonal antibody secukinumab in treatment of ankylosing spondylitis: a randomised, double-blind, placebo-controlled trial. Lancet. 2013;382(9906):1705–1713.

    Article  CAS  Google Scholar 

  22. Hueber W, Sands BE, Lewitzky S, et al.; Secukinumab in Crohn’s Disease Study Group. Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohn’s disease: unexpected results of a randomised, double-blind placebo-controlled trial. Gut. 2012;61(12):1693–700.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Development Biometrics & Statistical Science, Novartis Pharma AG, Postfach, 4002, Basel, Switzerland

    Roland Fisch PhD, Ieuan Jones BSc, Julie Jones MSc, Jouni Kerman PhD, Gerd Karl Rosenkranz PhD & Heinz Schmidli PhD

Authors
  1. Roland Fisch PhD
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  2. Ieuan Jones BSc
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  3. Julie Jones MSc
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  4. Jouni Kerman PhD
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  5. Gerd Karl Rosenkranz PhD
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  6. Heinz Schmidli PhD
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Correspondence to Roland Fisch PhD.

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Fisch, R., Jones, I., Jones, J. et al. Bayesian Design of Proof-of-Concept Trials. Ther Innov Regul Sci 49, 155–162 (2015). https://doi.org/10.1177/2168479014533970

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  • DOI: https://doi.org/10.1177/2168479014533970

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