IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v21y2025i1p53-65n1011.html
   My bibliography  Save this article

DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data

Author

Listed:
  • Zhang Haixiang

    (Center for Applied Mathematics and KL-AAGDM, 12605 Tianjin University , Tianjin 300072, China)

  • Li Yang

    (Department of Biostatistics and Health Data Science, Indiana University School of Medicine and Richard M. Fairbanks School of Public Health, Indianapolis, IN 46202, USA)

  • Wang HaiYing

    (Department of Statistics, University of Connecticut, Storrs, Mansfield, CT 06269, USA)

Abstract

To ensure privacy protection and alleviate computational burden, we propose a fast subsmaling procedure for the Cox model with massive survival datasets from multi-centered, decentralized sources. The proposed estimator is computed based on optimal subsampling probabilities that we derived and enables transmission of subsample-based summary level statistics between different storage sites with only one round of communication. For inference, the asymptotic properties of the proposed estimator were rigorously established. An extensive simulation study demonstrated that the proposed approach is effective. The methodology was applied to analyze a large dataset from the U.S. airlines.

Suggested Citation

  • Zhang Haixiang & Li Yang & Wang HaiYing, 2025. "DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data," The International Journal of Biostatistics, De Gruyter, vol. 21(1), pages 53-65.
    • Handle: RePEc:bpj:ijbist:v:21:y:2025:i:1:p:53-65:n:1011
    DOI: 10.1515/ijb-2024-0042
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2024-0042
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/ijb-2024-0042?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

    Keywords

    ;
    ;
    ;
    ;

    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:bpj:ijbist:v:21:y:2025:i:1:p:53-65:n:1011. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.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.