[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v17y2021i2p283-293n10.html
   My bibliography  Save this article

Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up

Author

Listed:
  • McVittie James

    (McGill University, Mathematics and Statistics, 805 Sherbrooke Street West, Montreal, Quebec Canada)

  • Wolfson David

    (McGill University, Mathematics and Statistics, 805 Sherbrooke Street West, Montreal, Quebec Canada)

  • Stephens David

    (McGill University, Mathematics and Statistics, 805 Sherbrooke Street West, Montreal, Quebec Canada)

  • Addona Vittorio

    (Macalester College, Mathematics, Statistics and Computer Science, St.Paul, Minnesota, United States)

  • Buckeridge David

    (McGill University, Epidemiology, Biostatistics and Occupational Health, Montreal, Quebec Canada)

Abstract
A classical problem in survival analysis is to estimate the failure time distribution from right-censored observations obtained from an incident cohort study. Frequently, however, failure time data comprise two independent samples, one from an incident cohort study and the other from a prevalent cohort study with follow-up, which is known to produce length-biased observed failure times. There are drawbacks to each of these two types of study when viewed separately. We address two main questions here: (i) Can our statistical inference be enhanced by combining data from an incident cohort study with data from a prevalent cohort study with follow-up? (ii) What statistical methods are appropriate for these combined data? The theory we develop to address these questions is based on a parametrically defined failure time distribution and is supported by simulations. We apply our methods to estimate the duration of hospital stays.

Suggested Citation

  • McVittie James & Wolfson David & Stephens David & Addona Vittorio & Buckeridge David, 2021. "Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up," The International Journal of Biostatistics, De Gruyter, vol. 17(2), pages 283-293, November.
    • Handle: RePEc:bpj:ijbist:v:17:y:2021:i:2:p:283-293:n:10
    DOI: 10.1515/ijb-2020-0042
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2020-0042
    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-2020-0042?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 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:bpj:ijbist:v:17:y:2021:i:2:p:283-293:n:10. 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.degruyter.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.