IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v52y2025i16p3036-3052.html

Optimal distributed subsampling for accelerated failure time models with massive censored data

Author

Listed:
  • Chunjie Wang
  • Jing Li
  • Xiaohui Yuan

Abstract

The availability of massive data stored across multiple locations is increasing in many fields. The data at each site often exhibits large-scale features. Current research primarily focuses on such datasets that consist of uncensored observations. As a popular model in survival analysis, the AFT model provides an intuitive explanation of survival times, making the model results easier to understand in practical applications. In this paper, we develop a distributed subsampling procedure specifically designed for accelerated failure time (AFT) model. The consistency and asymptotic normality of the resulting estimator are proved. A two-step algorithm is provided to address practical implementation issues and to determine both the optimal subsampling probabilities and allocation sizes. We conduct numerical simulation studies to evaluate the performance of our method and apply it to a lymphoma dataset.

Suggested Citation

  • Chunjie Wang & Jing Li & Xiaohui Yuan, 2025. "Optimal distributed subsampling for accelerated failure time models with massive censored data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 52(16), pages 3036-3052, December.
    • Handle: RePEc:taf:japsta:v:52:y:2025:i:16:p:3036-3052
    DOI: 10.1080/02664763.2025.2495717
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2025.2495717
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2025.2495717?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

    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:japsta:v:52:y:2025:i:16:p:3036-3052. 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/CJAS20 .

    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.