IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v46y2017i4p1916-1926.html
   My bibliography  Save this article

Linear transformation models for survival analysis with tumor growth information in cancer screening study

Author

Listed:
  • Pao-Sheng Shen

Abstract

The complication in analyzing tumor data is that the tumors detected in a screening program tend to be slowly progressive tumors, which is the so-called left-truncated sampling that is inherent in screening studies. Under the assumption that all subjects have the same tumor growth function, Ghosh (2008) developed estimation procedures for the Cox proportional hazards model. Shen (2011a) demonstrated that Ghosh (2008)'s approach can be extended to the case when each subject has a specific growth function. In this article, under linear transformation model, we present a general framework to the analysis of data from cancer screening studies. We developed estimation procedures under linear transformation model, which includes Cox's model as a special case. A simulation study is conducted to demonstrate the potential usefulness of the proposed estimators.

Suggested Citation

  • Pao-Sheng Shen, 2017. "Linear transformation models for survival analysis with tumor growth information in cancer screening study," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(4), pages 1916-1926, February.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:4:p:1916-1926
    DOI: 10.1080/03610926.2015.1030425
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2015.1030425
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2015.1030425?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:46:y:2017:i:4:p:1916-1926. 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.