IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v14y2022i2d10.1007_s12561-021-09332-4.html
   My bibliography  Save this article

Improving Early Futility Determination by Learning from External Data in Pediatric Cancer Clinical Trials

Author

Listed:
  • Jingjing Ye

    (Global Statistics and Data Sciences (GSDS), BeiGene (USA))

  • Gregory Reaman

    (U.S. Food and Drug Administration (FDA))

Abstract

Pediatric cancer consists of a diverse group of rare diseases. The relatively small population of children with multiple, disparate tumor types across various age groups presents a significant challenge for drug development programs as compared to oncology drug development programs for adults. A recent review paper searched the written requests that were issued by the US FDA between 2001 and 2019. Many of the completed pediatric trials over the past 19 years have led to conclusions that the cancer drugs developed for adult cancer indications have not demonstrated sufficient effectiveness within the context of limited phase 1 and/or phase 2 studies in heavily pretreated patients (Akalu et al. in Pediatr Blood Cancer. https://doi.org/10.1002/pbc.28828 , 2020). Faster learning and the implementation of futility criteria in the trial design should be considered in pediatric trials when the potential beneficial effects of investigational drugs may be unclear. In this paper, the authors compare the commonly used Simon’s 2-stage design in pediatric cancer trials to Bayesian sequential monitoring. The results show that the chance to stop for futility is at least doubled when a Bayesian design is used when compared to Simon’s 2-stage. The lower the true response rates are, the greater the number of patients would be saved from exposure to an ineffective treatment. To overcome the limitation of a small population and limited extrapolation opportunities, the innovative approach using Bayesian strategy to allow leveraging adult or external data in pediatric cancer trials should be considered.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stabio:v:14:y:2022:i:2:d:10.1007_s12561-021-09332-4
    DOI: 10.1007/s12561-021-09332-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-021-09332-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-021-09332-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. 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.
    3. Manuel Wiesenfarth & Silvia Calderazzo, 2020. "Quantification of prior impact in terms of effective current sample size," Biometrics, The International Biometric Society, vol. 76(1), pages 326-336, March.
    4. David J. Spiegelhalter & Laurence S. Freedman & Mahesh K. B. Parmar, 1994. "Bayesian Approaches to Randomized Trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 357-387, May.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Thomas A. Murray & Brian P. Hobbs & Theodore C. Lystig & Bradley P. Carlin, 2014. "Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical data," Biometrics, The International Biometric Society, vol. 70(1), pages 185-191, March.
    12. Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    13. Hui Quan & Xiaofei Chen & Xun Chen & Xiaodong Luo, 2022. "Assessments of Conditional and Unconditional Type I Error Probabilities for Bayesian Hypothesis Testing with Historical Data Borrowing," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 139-157, April.
    14. Charles F. Manski, 2018. "Reasonable patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1397-1421, October.
    15. Egidi, Leonardo, 2022. "Effective sample size for a mixture prior," Statistics & Probability Letters, Elsevier, vol. 183(C).
    16. Dan J. Spitzner, 2023. "Calibrated Bayes factors under flexible priors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 733-767, September.
    17. Qingyang Liu & Junxian Geng & Frank Fleischer & Qiqi Deng, 2022. "Efficacy-Driven Dose Finding with Toxicity Control in Phase I Oncology Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 413-431, December.
    18. Paul Gustafson & Nhu D. Le & Refik Saskin, 2001. "Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities," Biometrics, The International Biometric Society, vol. 57(2), pages 598-609, June.
    19. Charles F. Manski, 2021. "Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald," Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
    20. 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.

    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:spr:stabio:v:14:y:2022:i:2:d:10.1007_s12561-021-09332-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.