IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v16y2014i3d10.1007_s11009-013-9344-9.html
   My bibliography  Save this article

A Response-Driven Adaptive Design Based on the Klein Urn

Author

Listed:
  • Arkaitz Galbete

    (Universidad Pública de Navarra)

  • José A. Moler

    (Universidad Pública de Navarra)

  • Fernando Plo

    (Universidad de Zaragoza)

Abstract

The Klein design is a response-driven random rule to allocate experimental subjects between two treatments. It aims to allocate more subjects to the treatment that is performing better, and therefore it is useful when ethical issues are of prime interest. It behaves asymptotically as the drop-the-loser rule, which is known to have a high degree of compromise between ethics and inferential properties. Besides, the Klein design has a powerful stochastic structure, which permits to obtain exact values, for each sample size n, for its main operating characteristics, such as variability of allocations, expected failure rate and power, selection bias or accidental bias. These properties of the Klein design are thoroughly studied and we obtain exact and asymptotic results.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:metcap:v:16:y:2014:i:3:d:10.1007_s11009-013-9344-9
    DOI: 10.1007/s11009-013-9344-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11009-013-9344-9
    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/s11009-013-9344-9?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.
    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. 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.
    2. 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.
    3. Cai, Yong & Rafi, Ahnaf, 2024. "On the performance of the Neyman Allocation with small pilots," Journal of Econometrics, Elsevier, vol. 242(1).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Anna Paganoni & Piercesare Secchi, 2007. "A numerical study for comparing two response-adaptive designs for continuous treatment effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(3), pages 321-346, November.
    11. Jinglong Zhao, 2023. "Adaptive Neyman Allocation," Papers 2309.08808, arXiv.org, revised Sep 2023.
    12. 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.
    13. Xu, Wenfu & Gao, Jingya & Hu, Feifang & Cheung, Siu Hung, 2018. "Response-adaptive treatment allocation for non-inferiority trials with heterogeneous variances," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 168-179.
    14. Yi, Yanqing, 2013. "Exact statistical power for response adaptive designs," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 201-209.
    15. Yi, Yanqing & Wang, Xikui, 2023. "A Markov decision process for response adaptive designs," Econometrics and Statistics, Elsevier, vol. 25(C), pages 125-133.
    16. 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.

    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:metcap:v:16:y:2014:i:3:d:10.1007_s11009-013-9344-9. 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.