IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-10-0126-0_4.html
   My bibliography  Save this book chapter

A Comparative Study of Model-Based Dose-Finding Methods for Two-Agent Combination Trials

In: Frontiers of Biostatistical Methods and Applications in Clinical Oncology

Author

Listed:
  • Akihiro Hirakawa

    (The University of Tokyo, Department of Biostatistics and Bioinformatics, Graduate School of Medicine)

  • Hiroyuki Sato

    (Pharmaceuticals and Medical Devices Agency, Biostatistics Group, Office of New Drug V)

Abstract

Little is known about the relative relationships of the operating characteristics for rival model-based dose-finding methods for two-agent combination phase I trialsPhase I trial . In this chapter, we focus on the model-based dose-finding methods that have been recently developed. We compare the recommendation rates for true maximum tolerated dose combinations (MTDCs) and over dose combinations (ODCs) among these methods under 16 scenarios with 3 × 3, 4 × 4, 2 × 4, and 3 × 5 dose combination matrices through comprehensive simulationSimulation studies. We found that the operating characteristics of the dose-finding methods varied depending on (1) whether the dose combination matrix is square or not, (2) whether the true MTDCs exist within the same group consisting of the diagonals of the dose combination matrix, and (3) the number of true MTDCs. We also discuss the details of the operating characteristics and the advantages and disadvantages of the dose-finding methods compared.

Suggested Citation

  • Akihiro Hirakawa & Hiroyuki Sato, 2017. "A Comparative Study of Model-Based Dose-Finding Methods for Two-Agent Combination Trials," Springer Books, in: Shigeyuki Matsui & John Crowley (ed.), Frontiers of Biostatistical Methods and Applications in Clinical Oncology, pages 37-52, Springer.
    • Handle: RePEc:spr:sprchp:978-981-10-0126-0_4
    DOI: 10.1007/978-981-10-0126-0_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:spr:sprchp:978-981-10-0126-0_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.

    We have no bibliographic references for this item. You can help adding them by using 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.