IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i20p7243-7263.html
   My bibliography  Save this article

Joint modeling of longitudinal count and time-to-event data with excess zero using accelerated failure time model: an application with CD4 cell counts

Author

Listed:
  • Mojtaba Zeinali Najafabadi
  • Ehsan Bahrami Samani
  • Mojtaba Ganjali

Abstract

Longitudinal count and time to event (TTE) data are often associated in some ways. Hence, using joint models for analyzing these data constitutes an attractive modeling framework which is applied in many different fields of statistics and clinical studies. Also, Accelerated Failure Time (AFT) models can be used for the analysis of TTE data to estimate the effects of covariates on acceleration/deceleration of the survival time. So, we assume that the time variable is modeled in this article with Weibull AFT distribution. Furthermore, to develop the joint modeling strategy of these kinds of data, a correlated generalized linear mixed effect model (GLMEM) is applied using a member of the family of power series (PS) distributions in the longitudinal count submodel. Both of longitudinal count and TTE data may have excess zeros. The adequate of this approach in joint modeling with considering two censoring mechanism, right and left is illustrated using some simulation studies. Finally, we implement these proposed joint models on real AIDS data set.

Suggested Citation

  • Mojtaba Zeinali Najafabadi & Ehsan Bahrami Samani & Mojtaba Ganjali, 2022. "Joint modeling of longitudinal count and time-to-event data with excess zero using accelerated failure time model: an application with CD4 cell counts," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(20), pages 7243-7263, October.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:20:p:7243-7263
    DOI: 10.1080/03610926.2021.1872635
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1872635
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1872635?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.

    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:taf:lstaxx:v:51:y:2022:i:20:p:7243-7263. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.