IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v78y2022i4p1651-1661.html
   My bibliography  Save this article

Flexible use of copula‐type model for dose‐finding in drug combination clinical trials

Author

Listed:
  • Koichi Hashizume
  • Jun Tshuchida
  • Takashi Sozu

Abstract

Identification of the maximum tolerated dose combination (MTDC) of cancer drugs is an important objective in phase I oncology trials. Numerous dose‐finding designs for drug combination have been proposed over the years. Copula‐type models exhibit distinctive advantages in this task over other models used in existing competitive designs. For example, their application enables the consideration of dose‐limiting toxicities attributable to one of two agents. However, if a particular combination therapy demonstrates extremely synergistic toxicity, copula‐type models are liable to induce biases in toxicity probability estimators due to the associated Fréchet–Hoeffding bounds. Consequently, the dose‐finding performance may be worse than those of other competitive designs. The objective of this study is to improve the performance of dose‐finding designs based on copula‐type models while maintaining their advantageous properties. We propose an extension of the parameter space of the interaction term in copula‐type models. This releases the Fréchet–Hoeffding bounds, making the estimation of toxicity probabilities more flexible. Numerical examples in various scenarios demonstrate that the performance (e.g., the percentage of correct MTDC selection) of the proposed method is better than those exhibited by existing copula‐type models and comparable with those of other competitive designs, irrespective of the existence of extreme synergistic toxicity. The results obtained in this study could motivate the real‐world application of the proposed method in cases requiring the utilization of the properties of copula‐type models.

Suggested Citation

  • Koichi Hashizume & Jun Tshuchida & Takashi Sozu, 2022. "Flexible use of copula‐type model for dose‐finding in drug combination clinical trials," Biometrics, The International Biometric Society, vol. 78(4), pages 1651-1661, December.
  • Handle: RePEc:bla:biomet:v:78:y:2022:i:4:p:1651-1661
    DOI: 10.1111/biom.13510
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13510
    Download Restriction: no

    File URL: https://libkey.io/10.1111/biom.13510?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
    ---><---

    References listed on IDEAS

    as
    1. Nolan A. Wages & Mark R. Conaway & John O'Quigley, 2011. "Continual Reassessment Method for Partial Ordering," Biometrics, The International Biometric Society, vol. 67(4), pages 1555-1563, December.
    2. Guosheng Yin & Ying Yuan, 2010. "Authors’ response," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 544-546, May.
    3. Guosheng Yin & Ying Yuan, 2009. "A Latent Contingency Table Approach to Dose Finding for Combinations of Two Agents," Biometrics, The International Biometric Society, vol. 65(3), pages 866-875, September.
    4. Mauro Gasparini & Stuart Bailey & Beat Neuenschwander, 2010. "Bayesian dose finding in oncology for drug combinations by copula regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 543-544, May.
    5. Ali, Mir M. & Mikhail, N. N. & Haq, M. Safiul, 1978. "A class of bivariate distributions including the bivariate logistic," Journal of Multivariate Analysis, Elsevier, vol. 8(3), pages 405-412, September.
    6. Mauro Gasparini, 2013. "General classes of multiple binary regression models in dose finding problems for combination therapies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(1), pages 115-133, January.
    7. Guosheng Yin & Ying Yuan, 2009. "Bayesian dose finding in oncology for drug combinations by copula regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 211-224, 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. Márcio A. Diniz & Sungjin Kim & Mourad Tighiouart, 2020. "A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades," Stats, MDPI, vol. 3(3), pages 1-18, July.
    2. Beibei Guo & Elizabeth Garrett‐Mayer & Suyu Liu, 2021. "A Bayesian phase I/II design for cancer clinical trials combining an immunotherapeutic agent with a chemotherapeutic agent," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1210-1229, November.
    3. Hengzhen Huang & Hong†Bin Fang & Ming T. Tan, 2018. "Experimental design for multi†drug combination studies using signaling networks," Biometrics, The International Biometric Society, vol. 74(2), pages 538-547, June.
    4. 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.
    5. Beibei Guo & Suyu Liu, 2018. "Optimal Benchmark for Evaluating Drug-Combination Dose-Finding Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 184-201, April.
    6. Nolan A. Wages & Mark R. Conaway & John O'Quigley, 2011. "Continual Reassessment Method for Partial Ordering," Biometrics, The International Biometric Society, vol. 67(4), pages 1555-1563, December.
    7. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    8. Mauro Gasparini & Stuart Bailey & Beat Neuenschwander, 2010. "Bayesian dose finding in oncology for drug combinations by copula regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 543-544, May.
    9. Pavel Mozgunov & Rochelle Knight & Helen Barnett & Thomas Jaki, 2021. "Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study," IJERPH, MDPI, vol. 18(1), pages 1-19, January.
    10. Sean M. Devlin & Alexia Iasonos & John O’Quigley, 2021. "Phase I clinical trials in adoptive T‐cell therapies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 815-834, August.
    11. Naifar, Nader & Hammoudeh, Shawkat & Al dohaiman, Mohamed S., 2016. "Dependence structure between sukuk (Islamic bonds) and stock market conditions: An empirical analysis with Archimedean copulas," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 148-165.
    12. Thomas M. Braun, 2018. "Motivating sample sizes in adaptive Phase I trials via Bayesian posterior credible intervals," Biometrics, The International Biometric Society, vol. 74(3), pages 1065-1071, September.
    13. Monia Ezzalfani, 2019. "How to design a dose-finding study on combined agents: Choice of design and development of R functions," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-24, November.
    14. Flavia Gesualdi & Niklas Wahl, 2024. "Cumulative Histograms under Uncertainty: An Application to Dose–Volume Histograms in Radiotherapy Treatment Planning," Stats, MDPI, vol. 7(1), pages 1-17, March.
    15. Hossein Nadeb & Hamzeh Torabi & Ali Dolati, 2018. "Stochastic comparisons of the largest claim amounts from two sets of interdependent heterogeneous portfolios," Papers 1812.08343, arXiv.org.
    16. Manuel Gonzalez-Astudillo, 2013. "Monetary-fiscal policy interactions: interdependent policy rule coefficients," Finance and Economics Discussion Series 2013-58, Board of Governors of the Federal Reserve System (U.S.).
    17. Cuadras, Carles M., 2015. "Contributions to the diagonal expansion of a bivariate copula with continuous extensions," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 28-44.
    18. Faugeras, Olivier P., 2009. "A quantile-copula approach to conditional density estimation," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2083-2099, October.
    19. Omey, Edward & Vesilo, R., 2009. "Random Sums of Random Variables and Vectors," Working Papers 2009/09, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
    20. M. Vrac & L. Billard & E. Diday & A. Chédin, 2012. "Copula analysis of mixture models," Computational Statistics, Springer, vol. 27(3), pages 427-457, September.

    More about this item

    Statistics

    Access and download statistics

    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:bla:biomet:v:78:y:2022:i:4:p:1651-1661. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.