IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i5d10.1007_s00180-024-01575-1.html
   My bibliography  Save this article

Multiple-response logistic regression modeling with application to an analysis of cirrhosis liver disease data

Author

Listed:
  • Yang Jing-Nan

    (Northwest Normal University
    Gansu Provincial Research Center for Basic Disciplines of Mathematics and Statistics)

  • Tian Yu-Zhu

    (Northwest Normal University
    Gansu Provincial Research Center for Basic Disciplines of Mathematics and Statistics)

  • Wang Yue

    (The Education University of Hong Kong)

  • Wu Chun-Ho

    (The Hang Seng University of Hong Kong)

Abstract

In practical data analysis, individual measurements usually include two or more responses, and some statistical correlations often exist between the responses. Especially in medical data analysis, observations are often binary responses. A class of multi-response logistic regression model based on a joint modeling approach is investigated in this paper, and an application to a group data of primary biliary cirrhosis diseases is considered. Firstly, we propose a new class of multi-response logistic distribution and investigate its statistical properties. Secondly, a multi-response logistic regression model is constructed using a latent variable model and multi-variate logistic error distribution. Furthermore, the parameter estimation method of the model is provided by applying the monte carlo expectation maximization (MCEM) algorithm and the multiple imputation method. Finally, numerical simulations and comparative predictions on a test set are performed to validate the finite sample performance of the proposed model, and the model is applied to a cirrhosis disease dataset for analysis.

Suggested Citation

  • Yang Jing-Nan & Tian Yu-Zhu & Wang Yue & Wu Chun-Ho, 2025. "Multiple-response logistic regression modeling with application to an analysis of cirrhosis liver disease data," Computational Statistics, Springer, vol. 40(5), pages 2611-2634, June.
  • Handle: RePEc:spr:compst:v:40:y:2025:i:5:d:10.1007_s00180-024-01575-1
    DOI: 10.1007/s00180-024-01575-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01575-1
    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/s00180-024-01575-1?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.

    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:compst:v:40:y:2025:i:5:d:10.1007_s00180-024-01575-1. 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: 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.