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    A beta regression approach to nonparametric longitudinal data classification in clinical trials.

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    Date
    2022-04-07
    Author
    Hernandez, Roberto Sergio, 1995-
    0000-0003-3143-682X
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    Abstract
    Classification is an important topic in statistical analysis. For example, in applications involving clinical trials, an often seen objective is to determine whether or not novel medicines and treatments differ from existing standards of care. There are numerous methods and approaches in the literature for this problem when the endpoint of interest is normally distributed or can be approximated by an asymptotic Normal distribution, yet, the approaches when using a non-normally distributed endpoint are limited. This is especially true when these endpoints are correlated across time. In this dissertation, we investigated several techniques for use with longitudinal, repeated measures data where there is a special interest in adapting some recent results found in the literature on Beta regression. The proposed methods provided a nonparametric, with regard to the design endpoint, model that can be used in the repeated measures problem.
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    https://hdl.handle.net/2104/11947
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    Copyright © Baylor® University All rights reserved. Legal Disclosures.
    Baylor University Waco, Texas 76798 1-800-BAYLOR-U
    Baylor University Libraries | One Bear Place #97148 | Waco, TX 76798-7148 | 254.710.2112 | Contact: libraryquestions@baylor.edu
    If you find any errors in content, please contact librarywebmaster@baylor.edu
    DSpace software copyright © 2002-2016  DuraSpace
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