IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v12y2020i2d10.1007_s12561-020-09272-5.html
   My bibliography  Save this article

A Curve-Free Bayesian Decision-Theoretic Design for Phase Ia/Ib Trials Considering Both Safety and Efficacy Outcomes

Author

Listed:
  • Shenghua Fan

    (California State University)

  • Bee Leng Lee

    (San Jose State University)

  • Ying Lu

    (Stanford University)

Abstract

A curve-free, Bayesian decision-theoretic two-stage design is proposed to select biological efficacious doses (BEDs) for phase Ia/Ib trials in which both toxicity and efficacy signals are observed. No parametric models are assumed to govern the dose–toxicity, dose–efficacy, and toxicity–efficacy relationships. We assume that the dose–toxicity curve is monotonic non-decreasing and the dose–efficacy curve is unimodal. In the phase Ia stage, a Bayesian model on the toxicity rates is used to locate the maximum tolerated dose. In the phase Ib stage, we model the dose–efficacy curve using a step function while continuing to monitor the toxicity rates. Furthermore, a measure of the goodness of fit of a candidate step function is proposed, and the interval of BEDs associated with the best fitting step function is recommended. At the end of phase Ib, if some doses are recommended as BEDs, a cohort of confirmation is recruited and assigned at these doses to improve the precision of estimates at these doses. Extensive simulation studies show that the proposed design has desirable operating characteristics across different shapes of the underlying true toxicity and efficacy curves.

Suggested Citation

  • Shenghua Fan & Bee Leng Lee & Ying Lu, 2020. "A Curve-Free Bayesian Decision-Theoretic Design for Phase Ia/Ib Trials Considering Both Safety and Efficacy Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 146-166, July.
  • Handle: RePEc:spr:stabio:v:12:y:2020:i:2:d:10.1007_s12561-020-09272-5
    DOI: 10.1007/s12561-020-09272-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-020-09272-5
    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/s12561-020-09272-5?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. Peter F. Thall & John D. Cook, 2004. "Dose-Finding Based on Efficacy–Toxicity Trade-Offs," Biometrics, The International Biometric Society, vol. 60(3), pages 684-693, September.
    2. Anastasia Ivanova, 2003. "A New Dose-Finding Design for Bivariate Outcomes," Biometrics, The International Biometric Society, vol. 59(4), pages 1001-1007, December.
    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.
    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. Bo Huang & Naitee Ting, 2020. "Introduction to Special Issue on ‘Statistical Methods for Cancer Immunotherapy’," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 79-82, July.

    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. Sergei Leonov & Bahjat Qaqish, 2020. "Correlated endpoints: simulation, modeling, and extreme correlations," Statistical Papers, Springer, vol. 61(2), pages 741-766, April.
    2. Peter F. Thall & Aniko Szabo & Hoang Q. Nguyen & Catherine M. Amlie-Lefond & Osama O. Zaidat, 2011. "Optimizing the Concentration and Bolus of a Drug Delivered by Continuous Infusion," Biometrics, The International Biometric Society, vol. 67(4), pages 1638-1646, December.
    3. Nadine Houede & Peter F. Thall & Hoang Nguyen & Xavier Paoletti & Andrew Kramar, 2010. "Utility-Based Optimization of Combination Therapy Using Ordinal Toxicity and Efficacy in Phase I/II Trials," Biometrics, The International Biometric Society, vol. 66(2), pages 532-540, June.
    4. Peter F. Thall & Hoang Q. Nguyen & Ralph G. Zinner, 2017. "Parametric dose standardization for optimizing two-agent combinations in a phase I–II trial with ordinal outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 201-224, January.
    5. Guosheng Yin & Yisheng Li & Yuan Ji, 2006. "Bayesian Dose-Finding in Phase I/II Clinical Trials Using Toxicity and Efficacy Odds Ratios," Biometrics, The International Biometric Society, vol. 62(3), pages 777-787, September.
    6. Peter F. Thall & Hoang Q. Nguyen & Elihu H. Estey, 2008. "Patient-Specific Dose Finding Based on Bivariate Outcomes and Covariates," Biometrics, The International Biometric Society, vol. 64(4), pages 1126-1136, December.
    7. 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.
    8. 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.
    9. 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.
    10. Matthew Reimherr & Xiao‐Li Meng & Dan L. Nicolae, 2021. "Prior sample size extensions for assessing prior impact and prior‐likelihood discordance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 413-437, July.
    11. Thomas A. Murray & Peter F. Thall & Ying Yuan & Sarah McAvoy & Daniel R. Gomez, 2017. "Robust Treatment Comparison Based on Utilities of Semi-Competing Risks in Non-Small-Cell Lung Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 11-23, January.
    12. Egidi, Leonardo, 2022. "Effective sample size for a mixture prior," Statistics & Probability Letters, Elsevier, vol. 183(C).
    13. Emma Gerard & Sarah Zohar & Hoai‐Thu Thai & Christelle Lorenzato & Marie‐Karelle Riviere & Moreno Ursino, 2022. "Bayesian dose regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics," Biometrics, The International Biometric Society, vol. 78(1), pages 300-312, March.
    14. Chunyan Cai & Ying Yuan & Yuan Ji, 2014. "A Bayesian dose finding design for oncology clinical trials of combinational biological agents," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 159-173, January.
    15. 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).
    16. Yinghui Zhou & John Whitehead & Pasi Korhonen & Mika Mustonen, 2008. "Implementation of a Bayesian Design in a Dose-Escalation Study of an Experimental Agent in Healthy Volunteers," Biometrics, The International Biometric Society, vol. 64(1), pages 299-308, March.
    17. Adam Fleischhacker & Pak-Wing Fok & Mokshay Madiman & Nan Wu, 2023. "A Closed-Form EVSI Expression for a Multinomial Data-Generating Process," Decision Analysis, INFORMS, vol. 20(1), pages 73-84, March.
    18. 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.
    19. B. Nebiyou Bekele & Yu Shen, 2005. "A Bayesian Approach to Jointly Modeling Toxicity and Biomarker Expression in a Phase I/II Dose-Finding Trial," Biometrics, The International Biometric Society, vol. 61(2), pages 343-354, June.
    20. Anastasia Ivanova & Se Hee Kim, 2009. "Dose Finding for Continuous and Ordinal Outcomes with a Monotone Objective Function: A Unified Approach," Biometrics, The International Biometric Society, vol. 65(1), pages 307-315, March.

    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:12:y:2020:i:2:d:10.1007_s12561-020-09272-5. 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.