IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v59y2003i2p420-429.html
   My bibliography  Save this article

Multiple Outputation: Inference for Complex Clustered Data by Averaging Analyses from Independent Data

Author

Listed:
  • Dean Follmann
  • Michael Proschan
  • Eric Leifer

Abstract

No abstract is available for this item.

Suggested Citation

  • Dean Follmann & Michael Proschan & Eric Leifer, 2003. "Multiple Outputation: Inference for Complex Clustered Data by Averaging Analyses from Independent Data," Biometrics, The International Biometric Society, vol. 59(2), pages 420-429, June.
    • Handle: RePEc:bla:biomet:v:59:y:2003:i:2:p:420-429
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/1541-0420.00049
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiuyu J. Cong & Guosheng Yin & Yu Shen, 2007. "Marginal Analysis of Correlated Failure Time Data with Informative Cluster Sizes," Biometrics, The International Biometric Society, vol. 63(3), pages 663-672, September.
    2. Follmann, Dean & Proschan, Michael, 2012. "A test of location for exchangeable multivariate normal data with unknown correlation," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 115-125, February.
    3. Dean Follmann & Chiung‐Yu Huang, 2018. "Sieve analysis using the number of infecting pathogens," Biometrics, The International Biometric Society, vol. 74(3), pages 1023-1033, September.
    4. Oluwafemi P Owodunni & Elliott R Haut & Dauryne L Shaffer & Deborah B Hobson & Jiangxia Wang & Gayane Yenokyan & Peggy S Kraus & Jonathan K Aboagye & Katherine L Florecki & Kristen L W Webster & Chris, 2020. "Using electronic health record system triggers to target delivery of a patient-centered intervention to improve venous thromboembolism prevention for hospitalized patients: Is there a differential eff," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-14, January.
    5. Joanna H. Shih & Michael P. Fay, 2017. "Pearson's chi-square test and rank correlation inferences for clustered data," Biometrics, The International Biometric Society, vol. 73(3), pages 822-834, September.
    6. S. Haneuse & J. Chen, 2011. "A Multiphase Design Strategy for Dealing with Participation Bias," Biometrics, The International Biometric Society, vol. 67(1), pages 309-318, March.

    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:bla:biomet:v:59:y:2003:i:2:p:420-429. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.