IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i16p8816-d618740.html
   My bibliography  Save this article

Bayesian Sequential Monitoring of Single-Arm Trials: A Comparison of Futility Rules Based on Binary Data

Author

Listed:
  • Valeria Sambucini

    (Dipartimento di Scienze Statistiche, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy)

Abstract

In clinical trials, futility rules are widely used to monitor the study while it is in progress, with the aim of ensuring early termination if the experimental treatment is unlikely to provide the desired level of efficacy. In this paper, we focus on Bayesian strategies to perform interim analyses in single-arm trials based on a binary response variable. Designs that exploit both posterior and predictive probabilities are described and a slight modification of the futility rules is introduced when a fixed historical response rate is used, in order to add uncertainty in the efficacy probability of the standard treatment through the use of prior distributions. The stopping boundaries of the designs are compared under the same trial settings and simulation studies are performed to evaluate the operating characteristics when analogous procedures are used to calibrate the probability cut-offs of the different decision rules.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8816-:d:618740
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/16/8816/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/16/8816/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    4. 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.
    5. Fulvio De Santis, 2007. "Using historical data for Bayesian sample size determination," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 95-113, January.
    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. 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.
    2. 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.
    3. Wenqing Li & Ming-Hui Chen & Xiaojing Wang & Dipak K. Dey, 2018. "Bayesian Design of Non-inferiority Clinical Trials Via the Bayes Factor," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 439-459, August.
    4. Bhramar Mukherjee & Jaeil Ahn & Stephen B. Gruber & Malay Ghosh & Nilanjan Chatterjee, 2010. "Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis," Biometrics, The International Biometric Society, vol. 66(3), pages 934-948, September.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Ming-Hui Chen & Joseph G. Ibrahim & Peter Lam & Alan Yu & Yuanye Zhang, 2011. "Bayesian Design of Noninferiority Trials for Medical Devices Using Historical Data," Biometrics, The International Biometric Society, vol. 67(3), pages 1163-1170, September.
    10. Alessandra Giovagnoli, 2021. "The Bayesian Design of Adaptive Clinical Trials," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    11. Waverly Wei & Xinwei Ma & Jingshen Wang, 2023. "Fair Adaptive Experiments," Papers 2310.16290, arXiv.org.
    12. 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.
    13. 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.
    14. 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.
    15. Jörg Martin & Clemens Elster, 2021. "The variation of the posterior variance and Bayesian sample size determination," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1135-1155, October.
    16. 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.
    17. 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.
    18. Danila Azzolina & Paola Berchialla & Silvia Bressan & Liviana Da Dalt & Dario Gregori & Ileana Baldi, 2022. "A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method," IJERPH, MDPI, vol. 19(21), pages 1-15, October.
    19. Pierpaolo Brutti & Fulvio Santis & Stefania Gubbiotti, 2014. "Bayesian-frequentist sample size determination: a game of two priors," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 133-151, August.
    20. 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.

    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:16:p:8816-:d:618740. 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.