IDEAS home Printed from https://ideas.repec.org/a/taf/tstfxx/v4y2020i1p97-108.html
   My bibliography  Save this article

Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk

Author

Listed:
  • Yijun Wang
  • Jiajia Zhang
  • Yincai Tang

Abstract

The mixture cure model is the most popular model used to analyse the major event with a potential cure fraction. But in the real world there may exist a potential risk from other non-curable competing events. In this paper, we study the accelerated failure time model with mixture cure model via kernel-based nonparametric maximum likelihood estimation allowing non-curable competing risk. An EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error densities, in which a kernel-smoothed conditional profile likelihood is maximised in the M-step, and the resulting estimates are consistent. Its performance is demonstrated through comprehensive simulation studies. Finally, the proposed method is applied to the colorectal clinical trial data.

Suggested Citation

  • Yijun Wang & Jiajia Zhang & Yincai Tang, 2020. "Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 4(1), pages 97-108, July.
    • Handle: RePEc:taf:tstfxx:v:4:y:2020:i:1:p:97-108
    DOI: 10.1080/24754269.2019.1600123
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24754269.2019.1600123
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24754269.2019.1600123?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.

    Citations

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


    Cited by:

    1. Ana Ezquerro & Brais Cancela & Ana López-Cheda, 2023. "On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility," Mathematics, MDPI, vol. 11(19), pages 1-21, October.

    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:tstfxx:v:4:y:2020:i:1:p:97-108. 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/tstf .

    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.