IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v7y2011i1n3.html
   My bibliography  Save this article

A Dunnett-Type Procedure for Multiple Endpoints

Author

Listed:
  • Hasler Mario

    (Christian-Albrechts-Universität zu Kiel)

  • Hothorn Ludwig A

    (Leibniz Universität Hannover)

Abstract

This paper describes a method for comparisons of several treatments with a control, simultaneously for multiple endpoints. These endpoints are assumed to be normally distributed with different scales and variances. An approximate multivariate t-distribution is used to obtain quantiles for test decisions, multiplicity-adjusted p-values, and simultaneous confidence intervals. Simulation results show that this approach controls the family-wise error type I over both the comparisons and the endpoints in an admissible range. The approach will be applied to a randomized clinical trial comparing two new sets of extracorporeal circulations with a standard for three primary endpoints. A related R package is available.

Suggested Citation

  • Hasler Mario & Hothorn Ludwig A, 2011. "A Dunnett-Type Procedure for Multiple Endpoints," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-15, January.
    • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:3
    DOI: 10.2202/1557-4679.1258
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1258
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1258?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. Bretz, Frank, 2006. "An extension of the Williams trend test to general unbalanced linear models," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1735-1748, April.
    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. Hasler Mario, 2013. "Multiple Contrasts for Repeated Measures," The International Journal of Biostatistics, De Gruyter, vol. 9(1), pages 1-13, July.
    2. Hothorn Ludwig A & Sill Martin & Schaarschmidt Frank, 2010. "Evaluation of Incidence Rates in Pre-Clinical Studies Using a Williams-Type Procedure," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-19, April.
    3. Philip Pallmann & Ludwig Hothorn & Gemechis Djira, 2014. "A Levene-type test of homogeneity of variances against ordered alternatives," Computational Statistics, Springer, vol. 29(6), pages 1593-1608, December.
    4. Kitsche, A. & Hothorn, L.A. & Schaarschmidt, F., 2012. "The use of historical controls in estimating simultaneous confidence intervals for comparisons against a concurrent control," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3865-3875.
    5. Mondal, Anjana & Sattler, Paavo & Kumar, Somesh, 2023. "Testing against ordered alternatives in a two-way model without interaction under heteroscedasticity," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    6. Philip Pallmann & Ludwig A. Hothorn, 2016. "Analysis of means: a generalized approach using R," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1541-1560, June.
    7. Mario Hasler, 2016. "Heteroscedasticity: multiple degrees of freedom vs. sandwich estimation," Statistical Papers, Springer, vol. 57(1), pages 55-68, March.
    8. Konietschke, Frank & Placzek, Marius & Schaarschmidt, Frank & Hothorn, Ludwig A., 2015. "nparcomp: An R Software Package for Nonparametric Multiple Comparisons and Simultaneous Confidence Intervals," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i09).
    9. Bogomolov, Marina & Davidov, Ori, 2019. "Order restricted univariate and multivariate inference with adjustment for covariates in partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 20-27.

    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:bpj:ijbist:v:7:y:2011:i:1:n:3. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.