IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v50y2021i5p1161-1172.html
   My bibliography  Save this article

Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point

Author

Listed:
  • Vicente G. Cancho
  • Gladys Barriga
  • Jeremias Leão
  • Helton Saulo

Abstract

Frailty models are used for modeling heterogeneity in the data analysis of lifetimes. Analysis that ignore frailty when it is present leads to incorrect inferences. In survival analysis, the distribution of frailty is generally assumed to be continuous and, in some cases, it may be appropriate to consider a discrete frailty distribution. Survival models induced by frailty with a continuous distribution are not appropriate for situations in which survival data contain experimental units where the event of interest has not happened even after a long period of observation (survival data with cure fraction), that is, situations with units having zero frailty. In this paper, we propose a new survival model induced by discrete frailty for modeling survival data in the presence of a proportion of long-term survivors and a single change point. We use the maximum likelihood method to estimate the model parameters and evaluate their performance by a Monte Carlo simulation study. The proposed approach is illustrated by analyzing a kidney infection recurrence data set.

Suggested Citation

  • Vicente G. Cancho & Gladys Barriga & Jeremias Leão & Helton Saulo, 2021. "Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(5), pages 1161-1172, March.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:5:p:1161-1172
    DOI: 10.1080/03610926.2019.1648826
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2019.1648826
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2019.1648826?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:50:y:2021:i:5:p:1161-1172. 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.