IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v132y2019icp18-30.html
   My bibliography  Save this article

Bayesian predictive monitoring with bivariate binary outcomes in phase II clinical trials

Author

Listed:
  • Sambucini, Valeria

Abstract

Traditionally, phase II single-arm trials are based on a binary response variable that represents the efficacy of the experimental treatment. However, the introduction of an additional binary endpoint to assess whether the new therapy is also sufficiently safe for a further evaluation in larger phase III studies is often suggested. A Bayesian predictive strategy for interim monitoring in phase II trials focused on bivariate binary outcomes is proposed. At any interim analysis, the stopping rules are based on the evaluation of the predictive probability that the trial will show a conclusive result at the planned end of the study, given the observed data. The proposed procedure is applied using hypothetical scenarios that represent different situations which may occur at the interim stage. A real data application is also illustrated with the use of both non-informative and informative prior distributions. Finally, simulation studies to evaluate the operating characteristics of the design have been performed.

Suggested Citation

  • Sambucini, Valeria, 2019. "Bayesian predictive monitoring with bivariate binary outcomes in phase II clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 18-30.
    • Handle: RePEc:eee:csdana:v:132:y:2019:i:c:p:18-30
    DOI: 10.1016/j.csda.2018.06.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947318301610
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2018.06.015?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. Guosheng Yin & Nan Chen & J. Jack Lee, 2012. "Phase II trial design with Bayesian adaptive randomization and predictive probability," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(2), pages 219-235, March.
    2. Yong Lin & Weichung J. Shih, 2004. "Adaptive Two-Stage Designs for Single-Arm Phase IIA Cancer Clinical Trials," Biometrics, The International Biometric Society, vol. 60(2), pages 482-490, June.
    3. Nigel Stallard & Peter F. Thall & John Whitehead, 1999. "Decision Theoretic Designs for Phase II Clinical Trials with Multiple Outcomes," Biometrics, The International Biometric Society, vol. 55(3), pages 971-977, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fulvio De Santis & Stefania Gubbiotti, 2021. "Sample Size Requirements for Calibrated Approximate Credible Intervals for Proportions in Clinical Trials," IJERPH, MDPI, vol. 18(2), pages 1-11, January.
    2. Valeria Sambucini, 2021. "Bayesian Sequential Monitoring of Single-Arm Trials: A Comparison of Futility Rules Based on Binary Data," IJERPH, MDPI, vol. 18(16), pages 1-17, August.
    3. Valeria Sambucini, 2021. "Efficacy and toxicity monitoring via Bayesian predictive probabilities in phase II clinical trials," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 637-663, June.

    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. Kim, Seongho & Wong, Weng Kee, 2022. "Spatial two-stage designs for phase II clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    2. Lurdes Y. T. Inoue & Peter F. Thall & Donald A. Berry, 2002. "Seamlessly Expanding a Randomized Phase II Trial to Phase III," Biometrics, The International Biometric Society, vol. 58(4), pages 823-831, December.
    3. Patricia Gilholm & Kerrie Mengersen & Helen Thompson, 2020. "Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-17, June.
    4. Yan‐Cheng Chao & Thomas M. Braun & Roy N. Tamura & Kelley M. Kidwell, 2020. "A Bayesian group sequential small n sequential multiple‐assignment randomized trial," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 663-680, June.
    5. Cornelia U. Kunz & Meinhard Kieser, 2011. "Simon's minimax and optimal and Jung's admissible two-stage designs with or without curtailment," Stata Journal, StataCorp LP, vol. 11(2), pages 240-254, June.
    6. Alessandra Giovagnoli, 2021. "The Bayesian Design of Adaptive Clinical Trials," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    7. Waverly Wei & Xinwei Ma & Jingshen Wang, 2023. "Fair Adaptive Experiments," Papers 2310.16290, arXiv.org.
    8. Vishal Ahuja & John R. Birge, 2020. "An Approximation Approach for Response-Adaptive Clinical Trial Design," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 877-894, October.
    9. Nigel Stallard, 2003. "Decision-Theoretic Designs for Phase II Clinical Trials Allowing for Competing Studies," Biometrics, The International Biometric Society, vol. 59(2), pages 402-409, June.
    10. 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.
    11. Yanxun Xu & Lorenzo Trippa & Peter Müller & Yuan Ji, 2016. "Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 159-180, June.
    12. Valeria Sambucini, 2021. "Efficacy and toxicity monitoring via Bayesian predictive probabilities in phase II clinical trials," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 637-663, June.
    13. Steffen Ventz & Lorenzo Trippa, 2015. "Bayesian designs and the control of frequentist characteristics: A practical solution," Biometrics, The International Biometric Society, vol. 71(1), pages 218-226, March.
    14. Guosheng Yin & Nan Chen & J. Jack Lee, 2018. "Bayesian Adaptive Randomization and Trial Monitoring with Predictive Probability for Time-to-Event Endpoint," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 420-438, August.
    15. Amir Ali Nasrollahzadeh & Amin Khademi, 2022. "Dynamic Programming for Response-Adaptive Dose-Finding Clinical Trials," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1176-1190, March.
    16. Lingfang Xia & Qi Zhou & Yunong Gao & Wenjing Hu & Ge Lou & Hong Sun & Jianqing Zhu & Jin Shu & Xianfeng Zhou & Rong Sun & Xiaohua Wu, 2022. "A multicenter phase 2 trial of camrelizumab plus famitinib for women with recurrent or metastatic cervical squamous cell carcinoma," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    17. Stefan Englert & Meinhard Kieser, 2012. "Improving the Flexibility and Efficiency of Phase II Designs for Oncology Trials," Biometrics, The International Biometric Society, vol. 68(3), pages 886-892, September.
    18. Valeria Sambucini, 2021. "Bayesian Sequential Monitoring of Single-Arm Trials: A Comparison of Futility Rules Based on Binary Data," IJERPH, MDPI, vol. 18(16), pages 1-17, August.
    19. Meichun Ding & Gary L. Rosner & Peter Müller, 2008. "Bayesian Optimal Design for Phase II Screening Trials," Biometrics, The International Biometric Society, vol. 64(3), pages 886-894, September.
    20. Peter F. Thall & Hoang Q. Nguyen & Sarah Zohar & Pierre Maton, 2014. "Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 931-943, 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:eee:csdana:v:132:y:2019:i:c:p:18-30. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.