IDEAS home Printed from https://ideas.repec.org/a/spr/metrik/v69y2009i1p1-15.html
   My bibliography  Save this article

Drop-the-loser design in the presence of covariates

Author

Listed:
  • Uttam Bandyopadhyay
  • Atanu Biswas
  • Rahul Bhattacharya

Abstract

No abstract is available for this item.

Suggested Citation

  • Uttam Bandyopadhyay & Atanu Biswas & Rahul Bhattacharya, 2009. "Drop-the-loser design in the presence of covariates," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 69(1), pages 1-15, January.
    • Handle: RePEc:spr:metrik:v:69:y:2009:i:1:p:1-15
    DOI: 10.1007/s00184-008-0170-y
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00184-008-0170-y
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00184-008-0170-y?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. Hu, Feifang & Rosenberger, William F., 2003. "Optimality, Variability, Power: Evaluating Response-Adaptive Randomization Procedures for Treatment Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 671-678, January.
    2. William F. Rosenberger & Nigel Stallard & Anastasia Ivanova & Cherice N. Harper & Michelle L. Ricks, 2001. "Optimal Adaptive Designs for Binary Response Trials," Biometrics, The International Biometric Society, vol. 57(3), pages 909-913, September.
    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. Atkinson, Anthony C. & Biswas, Atanu, 2017. "Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses," LSE Research Online Documents on Economics 66761, London School of Economics and Political Science, LSE Library.
    2. Jennifer Proper & Thomas A. Murray, 2023. "An alternative metric for evaluating the potential patient benefit of response‐adaptive randomization procedures," Biometrics, The International Biometric Society, vol. 79(2), pages 1433-1445, June.
    3. Li-Xin, Zhang, 2006. "Asymptotic results on a class of adaptive multi-treatment designs," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 586-605, March.
    4. Chambaz Antoine & van der Laan Mark J., 2011. "Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Theoretical Study," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-32, January.
    5. Lanju Zhang & William F. Rosenberger, 2006. "Response-Adaptive Randomization for Clinical Trials with Continuous Outcomes," Biometrics, The International Biometric Society, vol. 62(2), pages 562-569, June.
    6. Atkinson, Anthony C. & Biswas, Atanu, 2017. "Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 297-310.
    7. Yi, Yanqing, 2013. "Exact statistical power for response adaptive designs," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 201-209.
    8. Yi, Yanqing & Wang, Xikui, 2023. "A Markov decision process for response adaptive designs," Econometrics and Statistics, Elsevier, vol. 25(C), pages 125-133.
    9. Uttam Bandyopadhyay & Atanu Biswas & Shirsendu Mukherjee, 2009. "Adaptive two-treatment two-period crossover design for binary treatment responses incorporating carry-over effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 13-33, March.
    10. Hengtao Zhang & Guosheng Yin, 2021. "Response‐adaptive rerandomization," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1281-1298, November.
    11. Alessandro Baldi Antognini & Marco Novelli & Maroussa Zagoraiou, 2022. "A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials," Statistical Papers, Springer, vol. 63(1), pages 157-180, February.
    12. Yusuke Narita, 2018. "Experiment-as-Market: Incorporating Welfare into Randomized Controlled Trials," Cowles Foundation Discussion Papers 2127r, Cowles Foundation for Research in Economics, Yale University, revised May 2019.
    13. Hanan Hammouri & Marwan Alquran & Ruwa Abdel Muhsen & Jaser Altahat, 2022. "Optimal Weighted Multiple-Testing Procedure for Clinical Trials," Mathematics, MDPI, vol. 10(12), pages 1-19, June.
    14. Yanqing Yi & Yuan Yuan, 2013. "An optimal allocation for response-adaptive designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 1996-2008, September.
    15. Biswas, Atanu & Bhattacharya, Rahul, 2010. "An optimal response-adaptive design with dual constraints," Statistics & Probability Letters, Elsevier, vol. 80(3-4), pages 177-185, February.
    16. Arkaitz Galbete & José A. Moler & Fernando Plo, 2014. "A Response-Driven Adaptive Design Based on the Klein Urn," Methodology and Computing in Applied Probability, Springer, vol. 16(3), pages 731-746, September.
    17. Mandal, Saumen & Biswas, Atanu & Trandafir, Paula Camelia & Islam Chowdhury, Mohammad Ziaul, 2013. "Optimal target allocation proportion for correlated binary responses in a 2×2 setup," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 1991-1997.
    18. Jianhua Hu & Hongjian Zhu & Feifang Hu, 2015. "A Unified Family of Covariate-Adjusted Response-Adaptive Designs Based on Efficiency and Ethics," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 357-367, March.
    19. Xuemin Gu & Nan Chen & Caimiao Wei & Suyu Liu & Vassiliki A. Papadimitrakopoulou & Roy S. Herbst & J. Jack Lee, 2016. "Bayesian Two-Stage Biomarker-Based Adaptive Design for Targeted Therapy Development," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 99-128, June.
    20. Alessandro Baldi Antognini & Alessandra Giovagnoli, 2006. "On the asymptotic inference for response-adaptive experiments," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 29-45.

    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:metrik:v:69:y:2009:i:1:p:1-15. 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.