IDEAS home Printed from https://ideas.repec.org/a/spr/sankhb/v79y2017i1d10.1007_s13571-015-0108-0.html
   My bibliography  Save this article

Optimal Test Statistics for Minimising not Cured Proportion in Adaptive Clinical Trial

Author

Listed:
  • Anupam Kundu

    (Indian Statistical Institute)

  • Nabaneet Das

    (Indian Statistical Institute)

  • Sayantan Chakraborty

    (Indian Statistical Institute)

  • Subir Kumar Bhandari

    (Indian Statistical Institute)

Abstract

In last several decades adaptive sequential binary design has been used with a goal to increase performance in estimation, testing of parameters and to reduce expected number of non-cured patients in the context of clinical trials.The procedures have been studied theoretically and also using simulation techniques in many papers. As for example play the winner rule, randomised play the winner rule, adaptive randomised play the winner rule have been studied extensively. Rosenberger et al. (Biometrics 57, 3, 909–913, 2001) considered different types of difference function of p A ̂ $ \hat {p_{A}}$ and p B ̂ $\hat {p_{B}}$ as the test statistic in adaptive sequential design for the purpose of better inference along with decreasing number of non-cured patients. In this paper we considered how to choose the optimal function to achieve the goals with better performance. Using extensive simulation studies we have supported our claim and have shown that our methods perform better than existing methods. Also in the methods given by us we have a choice on the proportion of non-cured patients which we can vary with the inferential goal in mind. This is a new approach in adaptive sequential design.

Suggested Citation

  • Anupam Kundu & Nabaneet Das & Sayantan Chakraborty & Subir Kumar Bhandari, 2017. "Optimal Test Statistics for Minimising not Cured Proportion in Adaptive Clinical Trial," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(1), pages 156-169, May.
    • Handle: RePEc:spr:sankhb:v:79:y:2017:i:1:d:10.1007_s13571-015-0108-0
    DOI: 10.1007/s13571-015-0108-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13571-015-0108-0
    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/s13571-015-0108-0?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. 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.
    2. D. Azriel & M. Mandel & Y. Rinott, 2012. "Optimal allocation to maximize the power of two-sample tests for binary response," Biometrika, Biometrika Trust, vol. 99(1), pages 101-113.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Cai, Yong & Rafi, Ahnaf, 2024. "On the performance of the Neyman Allocation with small pilots," Journal of Econometrics, Elsevier, vol. 242(1).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Yuan, Ao & Chai, Gen Xiang, 2008. "Optimal adaptive generalized Polya urn design for multi-arm clinical trials," Journal of Multivariate Analysis, Elsevier, vol. 99(1), pages 1-24, January.
    16. Biswas, Atanu & Bhattacharya, Rahul, 2011. "Optimal response-adaptive allocation designs in phase III clinical trials: Incorporating ethics in optimality," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1155-1160, August.
    17. Uttam Bandyopadhyay & Atanu Biswas, 2018. "Fixed-width confidence interval for covariate-adjusted response-adaptive designs," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(2), pages 353-371, April.
    18. 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.
    19. Sofía S. Villar & William F. Rosenberger, 2018. "Covariate†adjusted response†adaptive randomization for multi†arm clinical trials using a modified forward looking Gittins index rule," Biometrics, The International Biometric Society, vol. 74(1), pages 49-57, March.
    20. Chambaz Antoine & van der Laan Mark J., 2011. "Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Simulation Study," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-30, 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:sankhb:v:79:y:2017:i:1:d:10.1007_s13571-015-0108-0. 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.