IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i2p328-339.html
   My bibliography  Save this article

A flexible additive-multiplicative transformation mean model for recurrent event data

Author

Listed:
  • Yanbin Du
  • Yuan Lv

Abstract

Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a flexible semi-parametric additive-multiplicative transformation mean model for recurrent event data, which includes the multiplicative model and additive transformation model as special cases. The new model is flexible in that they allow for both additive and multiplicative covariates effects, and additive effects are allowed to be time-varying. The estimation of regression parameters in the model is given by using the idea of estimating equations, and the asymptotic properties of the resulting estimators are established. Numerical studies under different settings were conducted for assessing the proposed methodology and an application to a bladder cancer study is illustrated. The results suggest that they work well.

Suggested Citation

  • Yanbin Du & Yuan Lv, 2022. "A flexible additive-multiplicative transformation mean model for recurrent event data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(2), pages 328-339, January.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:2:p:328-339
    DOI: 10.1080/03610926.2020.1748654
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2020.1748654
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2020.1748654?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:2:p:328-339. 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.