IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v44y2015i19p4007-4048.html
   My bibliography  Save this article

Likelihood Inference for Flexible Cure Rate Models with Gamma Lifetimes

Author

Listed:
  • N. Balakrishnan
  • Suvra Pal

Abstract

A flexible cure rate survival model was developed by Rodrigues et al. (2009a) by assuming the competing cause variable to follow the Conway-Maxwell Poisson distribution. This model includes as special cases some of the well-known cure rate models. As the data obtained from cancer clinical trials are often right censored, the EM algorithm can be efficiently used to estimate the model parameters based on right censored data. In this paper, we consider the cure rate model developed by Rodrigues et al. (2009a) and by assuming the time-to-event to follow the gamma distribution, we develop exact likelihood inference based on the EM algorithm. An extensive Monte Carlo simulation study is performed to examine the method of inference developed. Model discrimination between different cure rate models is carried out by means of likelihood ratio test and Akaike and Bayesian information criteria. Finally, the proposed methodology is illustrated with a cutaneous melanoma data.

Suggested Citation

  • N. Balakrishnan & Suvra Pal, 2015. "Likelihood Inference for Flexible Cure Rate Models with Gamma Lifetimes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(19), pages 4007-4048, October.
  • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:19:p:4007-4048
    DOI: 10.1080/03610926.2014.964807
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2014.964807
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03610926.2014.964807?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. Suvra Pal & Yingwei Peng & Wisdom Aselisewine, 2024. "A new approach to modeling the cure rate in the presence of interval censored data," Computational Statistics, Springer, vol. 39(5), pages 2743-2769, July.
    2. Diego I. Gallardo & Yolanda M. Gómez & Héctor J. Gómez & María José Gallardo-Nelson & Marcelo Bourguignon, 2023. "The Slash Half-Normal Distribution Applied to a Cure Rate Model with Application to Bone Marrow Transplantation," Mathematics, MDPI, vol. 11(3), pages 1-16, January.
    3. Suvra Pal & Souvik Roy, 2021. "On the estimation of destructive cure rate model: A new study with exponentially weighted Poisson competing risks," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(3), pages 324-342, August.

    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:19:p:4007-4048. 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.