IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v16y2024i1d10.1007_s12561-023-09392-8.html
   My bibliography  Save this article

Efficient Estimation of Semiparametric Transformation Model with Interval-Censored Data in Two-Phase Cohort Studies

Author

Listed:
  • Fei Gao

    (Fred Hutchinson Cancer Center)

  • Kwun Chuen Gary Chan

    (University of Washington)

Abstract

Interval sampling and two-phase sampling have both been advocated for studying rare failure outcomes. With few exceptions focusing on specific designs such as the case-cohort design, they are often studied separately in the statistical literature and require different estimation procedures. We consider efficient estimation of interval-censored data collected in a two-phase sampling design using a localized nonparametric likelihood. An expectation maximization algorithm is proposed by exploiting multiple layers of data augmentation that handle transformation function, interval-censoring, and two-phase sampling structure simultaneously. We study the asymptotic properties of the estimators and conduct inference using profile likelihood. We illustrate the performance of the proposed estimator by simulations and an HIV vaccine trial.

Suggested Citation

  • Fei Gao & Kwun Chuen Gary Chan, 2024. "Efficient Estimation of Semiparametric Transformation Model with Interval-Censored Data in Two-Phase Cohort Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(1), pages 203-220, April.
    • Handle: RePEc:spr:stabio:v:16:y:2024:i:1:d:10.1007_s12561-023-09392-8
    DOI: 10.1007/s12561-023-09392-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-023-09392-8
    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/s12561-023-09392-8?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:16:y:2024:i:1:d:10.1007_s12561-023-09392-8. 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.