IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i3p1022-d486146.html
   My bibliography  Save this article

Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges

Author

Listed:
  • Moreno Ursino

    (Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, USPC, Université de Paris, F-75006 Paris, France
    F-CRIN PARTNERS Platform, AP-HP, Université de Paris, F-75010 Paris, France)

  • Nigel Stallard

    (Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK)

Abstract

The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients in the considered population, for example, about 1 in 1500 people in U.S.; about 1 in 2500 people in Japan; and fewer than 1 in 2000 people in Europe. There are between 6000 and 8000 rare diseases and the main issue in drug development is linked to the challenge of achieving robust evidence from clinical trials in small populations. A better use of all available information can help the development process and Bayesian statistics can provide a solid framework at the design stage, during the conduct of the trial, and at the analysis stage. The focus of this manuscript is to provide a review of Bayesian methods for sample size computation or reassessment during phase II or phase III trial, for response adaptive randomization and of for meta-analysis in rare disease. Challenges regarding prior distribution choice, computational burden and dissemination are also discussed.

Suggested Citation

  • Moreno Ursino & Nigel Stallard, 2021. "Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges," IJERPH, MDPI, vol. 18(3), pages 1-9, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1022-:d:486146
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/3/1022/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/3/1022/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brian P. Hobbs & Bradley P. Carlin & Sumithra J. Mandrekar & Daniel J. Sargent, 2011. "Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials," Biometrics, The International Biometric Society, vol. 67(3), pages 1047-1056, September.
    2. Stavros Nikolakopoulos & Ingeborg van der Tweel & Kit C. B. Roes, 2018. "Dynamic borrowing through empirical power priors that control type I error," Biometrics, The International Biometric Society, vol. 74(3), pages 874-880, September.
    3. Satoshi Morita & Peter F. Thall & Peter Müller, 2008. "Determining the Effective Sample Size of a Parametric Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 595-602, June.
    4. A. Racine & A. P. Grieve & H. Flühler & A. F. M. Smith, 1986. "Bayesian Methods in Practice: Experiences in the Pharmaceutical Industry," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(2), pages 93-120, June.
    5. Heinz Schmidli & Sandro Gsteiger & Satrajit Roychoudhury & Anthony O'Hagan & David Spiegelhalter & Beat Neuenschwander, 2014. "Robust meta-analytic-predictive priors in clinical trials with historical control information," Biometrics, The International Biometric Society, vol. 70(4), pages 1023-1032, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Kaplan & Jianshen Chen & Sinan Yavuz & Weicong Lyu, 2023. "Bayesian Dynamic Borrowing of Historical Information with Applications to the Analysis of Large-Scale Assessments," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 1-30, March.
    2. Danila Azzolina & Giulia Lorenzoni & Silvia Bressan & Liviana Da Dalt & Ileana Baldi & Dario Gregori, 2021. "Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    3. Peng Yang & Yuansong Zhao & Lei Nie & Jonathon Vallejo & Ying Yuan, 2023. "SAM: Self‐adapting mixture prior to dynamically borrow information from historical data in clinical trials," Biometrics, The International Biometric Society, vol. 79(4), pages 2857-2868, December.
    4. Heinz Schmidli & Sandro Gsteiger & Satrajit Roychoudhury & Anthony O'Hagan & David Spiegelhalter & Beat Neuenschwander, 2014. "Robust meta-analytic-predictive priors in clinical trials with historical control information," Biometrics, The International Biometric Society, vol. 70(4), pages 1023-1032, December.
    5. Wenlin Yuan & Ming-Hui Chen & John Zhong, 2022. "Flexible Conditional Borrowing Approaches for Leveraging Historical Data in the Bayesian Design of Superiority Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 197-215, July.
    6. Egidi, Leonardo, 2022. "Effective sample size for a mixture prior," Statistics & Probability Letters, Elsevier, vol. 183(C).
    7. Ghaderinezhad, Fatemeh & Ley, Christophe & Serrien, Ben, 2022. "The Wasserstein Impact Measure (WIM): A practical tool for quantifying prior impact in Bayesian statistics," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    8. Stavros Nikolakopoulos & Ingeborg van der Tweel & Kit C. B. Roes, 2018. "Dynamic borrowing through empirical power priors that control type I error," Biometrics, The International Biometric Society, vol. 74(3), pages 874-880, September.
    9. Jingjing Ye & Gregory Reaman, 2022. "Improving Early Futility Determination by Learning from External Data in Pediatric Cancer Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 337-351, July.
    10. Lanju Zhang & Zailong Wang & Li Wang & Lu Cui & Jeremy Sokolove & Ivan Chan, 2022. "A Simple Approach to Incorporating Historical Control Data in Clinical Trial Design and Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 216-236, July.
    11. Schmidli, Heinz & Neuenschwander, Beat & Friede, Tim, 2017. "Meta-analytic-predictive use of historical variance data for the design and analysis of clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 100-110.
    12. Chenghao Chu & Bingming Yi, 2021. "Dynamic historical data borrowing using weighted average," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1259-1280, November.
    13. Alexander Kaizer & John Kittelson, 2020. "Discussion on “Predictively Consistent Prior Effective Sample Sizes” by Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, and Anthony O'Hagan," Biometrics, The International Biometric Society, vol. 76(2), pages 588-590, June.
    14. Chen, Nan & Carlin, Bradley P. & Hobbs, Brian P., 2018. "Web-based statistical tools for the analysis and design of clinical trials that incorporate historical controls," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 50-68.
    15. Liyun Jiang & Lei Nie & Ying Yuan, 2023. "Elastic priors to dynamically borrow information from historical data in clinical trials," Biometrics, The International Biometric Society, vol. 79(1), pages 49-60, March.
    16. Ian Wadsworth & Lisa V. Hampson & Thomas Jaki & Graeme J. Sills & Anthony G. Marson & Richard Appleton, 2020. "A quantitative framework to inform extrapolation decisions in children," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 515-534, February.
    17. Andrea Arfè & Brian Alexander & Lorenzo Trippa, 2021. "Optimality of testing procedures for survival data in the nonproportional hazards setting," Biometrics, The International Biometric Society, vol. 77(2), pages 587-598, June.
    18. Beat Neuenschwander & Sebastian Weber & Heinz Schmidli & Anthony O'Hagan, 2020. "Predictively consistent prior effective sample sizes," Biometrics, The International Biometric Society, vol. 76(2), pages 578-587, June.
    19. Arnaud Monseur & Bradley P. Carlin & Bruno Boulanger & Andreea Seferian & Laurent Servais & Chris Freitag & Leen Thielemans, 2022. "Leveraging Natural History Data in One- and Two-Arm Hierarchical Bayesian Studies of Rare Disease Progression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 237-258, July.
    20. Roland Brown & Yingling Fan & Kirti Das & Julian Wolfson, 2021. "Iterated multisource exchangeability models for individualized inference with an application to mobile sensor data," Biometrics, The International Biometric Society, vol. 77(2), pages 401-412, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1022-:d:486146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.