IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v17y2025i2d10.1007_s12561-024-09441-w.html
   My bibliography  Save this article

Estimation in Multi-State Semi-Markov Models with a Cured Fraction and Masked Causes of Deaths

Author

Listed:
  • Yongho Lim

    (Memorial University of Newfoundland)

  • Candemir Cigsar

    (Memorial University of Newfoundland)

  • Yildiz E. Yilmaz

    (Memorial University of Newfoundland)

Abstract

Analyses of disease-free survival data for certain cancer types indicate that cohorts of patients treated for cancer consist of individuals who are susceptible to experience cancer-related events and individuals who are cured. Cured individuals do not experience any cancer-related event and eventually die due to other causes. Individuals who are not cured may die after experiencing cancer recurrence or without experiencing any recurrence. Cure status is a partially latent variable and is only known if a disease-related event, cancer recurrence, or cancer death is observed. Causes of some observed deaths may be masked. To model disease progression events, which are cancer recurrence and cancer death, we consider a multi-state model including partially latent cured and not cured states. We describe our modeling approach and discuss an inference method incorporating masked causes of deaths. Our method allows to identify factors associated with the risk of experiencing a disease-related event and with timing of disease events after the treatment of cancer.

Suggested Citation

  • Yongho Lim & Candemir Cigsar & Yildiz E. Yilmaz, 2025. "Estimation in Multi-State Semi-Markov Models with a Cured Fraction and Masked Causes of Deaths," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 17(2), pages 386-409, July.
    • Handle: RePEc:spr:stabio:v:17:y:2025:i:2:d:10.1007_s12561-024-09441-w
    DOI: 10.1007/s12561-024-09441-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-024-09441-w
    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/s12561-024-09441-w?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:stabio:v:17:y:2025:i:2:d:10.1007_s12561-024-09441-w. 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.