IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v34y2025i1d10.1007_s10260-024-00775-9.html
   My bibliography  Save this article

A maximum statistic for the one-sided location-scale alternative in the two-stage design

Author

Listed:
  • Hidetoshi Murakami

    (Tokyo University of Science)

  • Markus Neuhäuser

    (Koblenz University of Applied Sciences)

Abstract

An increase in location is typically accompanied by an increase in variability. Subsequently, the heteroscedasticity can indicate a treatment effect. Therefore, it may be appropriate to perform a location-scale test. A common statistic for a location-scale test is the sum of a location and scale statistic. As demonstrated by Neuhäuser (Biometri J 43:809–819, 2001), weighting the sum increases the power. Although weights cannot usually be reasonably selected a priori, a weighting is possible in an adaptive design using the information obtained in an interim analysis. Here, we propose an adaptive statistic that increases and stabilizes the power. The power performance in various situations for continuous and discrete distributions is investigated using Monte Carlo simulations, which reveal that the proposed statistic increases and stabilizes the power, thus rendering it a strong competitor to existing location-scale statistics. The new statistic is illustrated using real data.

Suggested Citation

  • Hidetoshi Murakami & Markus Neuhäuser, 2025. "A maximum statistic for the one-sided location-scale alternative in the two-stage design," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 34(1), pages 91-112, March.
    • Handle: RePEc:spr:stmapp:v:34:y:2025:i:1:d:10.1007_s10260-024-00775-9
    DOI: 10.1007/s10260-024-00775-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-024-00775-9
    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/s10260-024-00775-9?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Amitava Mukherjee & Marco Marozzi, 2019. "A class of percentile modified Lepage-type tests," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(6), pages 657-689, August.
    2. Murakami, Hidetoshi, 2007. "Lepage type statistic based on the modified Baumgartner statistic," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 5061-5067, June.
    3. Walter Lehmacher & Gernot Wassmer, 1999. "Adaptive Sample Size Calculations in Group Sequential Trials," Biometrics, The International Biometric Society, vol. 55(4), pages 1286-1290, December.
    4. Marco Marozzi, 2013. "Adaptive choice of scale tests in flexible two-stage designs with applications in experimental ecology and clinical trials," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(4), pages 747-762.
    5. Hidetoshi Murakami, 2016. "A moment generating function of a combination of linear rank tests and its asymptotic efficiency," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(4), pages 674-691, December.
    6. Yamaguchi, Hikaru & Murakami, Hidetoshi, 2023. "The multi-aspect tests in the presence of ties," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    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. Yamaguchi, Hikaru & Murakami, Hidetoshi, 2023. "The multi-aspect tests in the presence of ties," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    2. Jingjing Chen, 2019. "A Note of Adaptive Design in Clinical Trials," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 107-111, August.
    3. Martin X. Dunbar & Hani M. Samawi & Robert Vogel & Lili Yu, 2014. "Steady-state Gibbs sampler estimation for lung cancer data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 977-988, May.
    4. Georg Gutjahr & Werner Brannath & Peter Bauer, 2011. "An Approach to the Conditional Error Rate Principle with Nuisance Parameters," Biometrics, The International Biometric Society, vol. 67(3), pages 1039-1046, September.
    5. Marco Marozzi, 2014. "The multisample Cucconi test," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 209-227, June.
    6. Michael Rosenblum & Ethan X. Fang & Han Liu, 2020. "Optimal, two‐stage, adaptive enrichment designs for randomized trials, using sparse linear programming," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 749-772, July.
    7. Michael A. Proschan, 2006. "Discussions," Biometrics, The International Biometric Society, vol. 62(3), pages 674-676, September.
    8. W. Brannath & P. Bauer & W. Maurer & M. Posch, 2003. "Sequential Tests for Noninferiority and Superiority," Biometrics, The International Biometric Society, vol. 59(1), pages 106-114, March.
    9. Gregory P. Levin & Sarah C. Emerson & Scott S. Emerson, 2014. "An evaluation of inferential procedures for adaptive clinical trial designs with pre-specified rules for modifying the sample size," Biometrics, The International Biometric Society, vol. 70(3), pages 556-567, September.
    10. repec:jss:jstsof:28:i07 is not listed on IDEAS
    11. Rui Tang & Xiaoye Ma & Hui Yang & Michael Wolf, 2018. "Biomarker-Defined Subgroup Selection Adaptive Design for Phase III Confirmatory Trial with Time-to-Event Data: Comparing Group Sequential and Various Adaptive Enrichment Designs," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 371-404, August.
    12. Yeonhee Park & Suyu Liu & Peter F. Thall & Ying Yuan, 2022. "Bayesian group sequential enrichment designs based on adaptive regression of response and survival time on baseline biomarkers," Biometrics, The International Biometric Society, vol. 78(1), pages 60-71, March.
    13. Rene Schmidt & Andreas Faldum & Robert Kwiecien, 2018. "Adaptive designs for the one†sample log†rank test," Biometrics, The International Biometric Society, vol. 74(2), pages 529-537, June.
    14. Takuya Nishino & Hidetoshi Murakami, 2019. "The Cucconi statistic for Type-I censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(8), pages 903-929, November.
    15. Dominika Polko-Zając, 2019. "On Permutation Location–Scale Tests," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 153-166, December.
    16. Polko-Zając Dominika, 2019. "On Permutation Location–Scale Tests," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 153-166, December.
    17. Nigel Stallard, 2023. "Adaptive enrichment designs with a continuous biomarker," Biometrics, The International Biometric Society, vol. 79(1), pages 9-19, March.
    18. Stefan Englert & Meinhard Kieser, 2012. "Improving the Flexibility and Efficiency of Phase II Designs for Oncology Trials," Biometrics, The International Biometric Society, vol. 68(3), pages 886-892, September.
    19. Hanan Hammouri & Mohammed Ali & Marwan Alquran & Areen Alquran & Ruwa Abdel Muhsen & Belal Alomari, 2023. "Adaptive Multiple Testing Procedure for Clinical Trials with Urn Allocation," Mathematics, MDPI, vol. 11(18), pages 1-20, September.
    20. Parsons, Nick & Friede, Tim & Todd, Susan & Marquez, Elsa Valdes & Chataway, Jeremy & Nicholas, Richard & Stallard, Nigel, 2012. "An R package for implementing simulations for seamless phase II/III clinical trials using early outcomes for treatment selection," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1150-1160.
    21. Martin Hellmich, 2001. "Monitoring Clinical Trials with Multiple Arms," Biometrics, The International Biometric Society, vol. 57(3), pages 892-898, September.

    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:stmapp:v:34:y:2025:i:1:d:10.1007_s10260-024-00775-9. 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.