IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v45y2004i2p197-214.html
   My bibliography  Save this article

Design efficiency in dose finding studies

Author

Listed:
  • Paoletti, Xavier
  • O'Quigley, John
  • Maccario, Jean

Abstract

No abstract is available for this item.

Suggested Citation

  • Paoletti, Xavier & O'Quigley, John & Maccario, Jean, 2004. "Design efficiency in dose finding studies," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 197-214, March.
    • Handle: RePEc:eee:csdana:v:45:y:2004:i:2:p:197-214
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(02)00323-7
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Mauro Gasparini & Jeffrey Eisele, 2000. "A Curve-Free Method for Phase I Clinical Trials," Biometrics, The International Biometric Society, vol. 56(2), pages 609-615, 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. Oron Assaf P. & Azriel David & Hoff Peter D., 2011. "Dose-Finding Designs: The Role of Convergence Properties," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-17, October.
    2. 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.

    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. Linda M. Haines & Inna Perevozskaya & William F. Rosenberger, 2003. "Bayesian Optimal Designs for Phase I Clinical Trials," Biometrics, The International Biometric Society, vol. 59(3), pages 591-600, September.
    2. Tian, Guo-Liang & Ng, Kai Wang & Tan, Ming, 2008. "EM-type algorithms for computing restricted MLEs in multivariate normal distributions and multivariate t-distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4768-4778, June.
    3. 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.
    4. Ying Kuen Cheung, 2002. "On the Use of Nonparametric Curves in Phase I Trials with Low Toxicity Tolerance," Biometrics, The International Biometric Society, vol. 58(1), pages 237-240, March.
    5. 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.
    6. 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.
    7. Ying Yuan & Guosheng Yin, 2009. "Bayesian dose finding by jointly modelling toxicity and efficacy as time‐to‐event outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 719-736, December.
    8. Ying Kuen Cheung & Rick Chappell, 2002. "A Simple Technique to Evaluate Model Sensitivity in the Continual Reassessment Method," Biometrics, The International Biometric Society, vol. 58(3), pages 671-674, September.
    9. Mezzetti, Maura & Muliere, Pietro & Bulla, Paolo, 2007. "An application of reinforced urn processes to determining maximum tolerated dose," Statistics & Probability Letters, Elsevier, vol. 77(7), pages 740-747, April.
    10. John O'Quigley, 2002. "Curve-Free and Model-Based Continual Reassessment Method Designs," Biometrics, The International Biometric Society, vol. 58(1), pages 245-249, March.
    11. 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.
    12. Drovandi, Christopher C. & McGree, James M. & Pettitt, Anthony N., 2013. "Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 320-335.

    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:eee:csdana:v:45:y:2004:i:2:p:197-214. 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.