IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v44y2015i8p1537-1551.html
   My bibliography  Save this article

Regression Analysis of Left-truncated and Case I Interval-censored Data with the Additive Hazards Model

Author

Listed:
  • Peijie Wang
  • Xingwei Tong
  • Shishun Zhao
  • Jianguo Sun

Abstract

In recent years the analysis of interval-censored failure time data has attracted a great deal of attention and such data arise in many fields including demographical studies, economic and financial studies, epidemiological studies, social sciences, and tumorigenicity experiments. This is especially the case in medical studies such as clinical trials. In this article, we discuss regression analysis of one type of such data, Case I interval-censored data, in the presence of left-truncation. For the problem, the additive hazards model is employed and the maximum likelihood method is applied for estimations of unknown parameters. In particular, we adopt the sieve estimation approach that approximates the baseline cumulative hazard function by linear functions. The resulting estimates of regression parameters are shown to be consistent and efficient and have an asymptotic normal distribution. An illustrative example is provided.

Suggested Citation

  • Peijie Wang & Xingwei Tong & Shishun Zhao & Jianguo Sun, 2015. "Regression Analysis of Left-truncated and Case I Interval-censored Data with the Additive Hazards Model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(8), pages 1537-1551, April.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:8:p:1537-1551
    DOI: 10.1080/03610926.2014.944665
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2014.944665
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2014.944665?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fei Gao & Kwun Chuen Gary Chan, 2019. "Semiparametric regression analysis of length‐biased interval‐censored data," Biometrics, The International Biometric Society, vol. 75(1), pages 121-132, March.
    2. P. G. Sankaran & S. Prasad, 2017. "An Additive Risks Regression Model For Middle-Censored Lifetime Data," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 459-479, September.

    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:44:y:2015:i:8:p:1537-1551. 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.