IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v69y2007i3p285-306.html
   My bibliography  Save this article

Mixture cure survival models with dependent censoring

Author

Listed:
  • Yi Li
  • Ram C. Tiwari
  • Subharup Guha

Abstract

Summary. The paper is motivated by cure detection among the prostate cancer patients in the National Institutes of Health surveillance epidemiology and end results programme, wherein the main end point (e.g. deaths from prostate cancer) and the censoring causes (e.g. deaths from heart diseases) may be dependent. Although many researchers have studied the mixture survival model to analyse survival data with non‐negligible cure fractions, none has studied the mixture cure model in the presence of dependent censoring. To account for such dependence, we propose a more general cure model that allows for dependent censoring. We derive the cure models from the perspective of competing risks and model the dependence between the censoring time and the survival time by using a class of Archimedean copula models. Within this framework, we consider the parameter estimation, the cure detection and the two‐sample comparison of latency distributions in the presence of dependent censoring when a proportion of patients is deemed cured. Large sample results by using martingale theory are obtained. We examine the finite sample performance of the proposed methods via simulation and apply them to analyse the surveillance epidemiology and end results prostate cancer data.

Suggested Citation

  • Yi Li & Ram C. Tiwari & Subharup Guha, 2007. "Mixture cure survival models with dependent censoring," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 285-306, June.
    • Handle: RePEc:bla:jorssb:v:69:y:2007:i:3:p:285-306
    DOI: 10.1111/j.1467-9868.2007.00589.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9868.2007.00589.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9868.2007.00589.x?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
    ---><---

    Citations

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


    Cited by:

    1. Wei‐Wen Hsu & David Todem & KyungMann Kim, 2016. "A sup‐score test for the cure fraction in mixture models for long‐term survivors," Biometrics, The International Biometric Society, vol. 72(4), pages 1348-1357, December.
    2. Dongdong Li & X. Joan Hu & Mary L. McBride & John J. Spinelli, 2020. "Multiple event times in the presence of informative censoring: modeling and analysis by copulas," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 573-602, July.
    3. Zhao, Xiaobing & Zhou, Xian, 2012. "Estimation of medical costs by copula models with dynamic change of health status," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 480-491.
    4. Liu, Yi & Wang, Qihua, 2015. "Copula-graphic estimators for the marginal survival function with censoring indicators missing at random," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 101-110.
    5. Yuan Mengdie & Diao Guoqing, 2014. "Semiparametric Odds Rate Model for Modeling Short-Term and Long-Term Effects with Application to a Breast Cancer Genetic Study," The International Journal of Biostatistics, De Gruyter, vol. 10(2), pages 231-249, November.
    6. Zhao, XiaoBing & Zhou, Xian, 2010. "Applying copula models to individual claim loss reserving methods," Insurance: Mathematics and Economics, Elsevier, vol. 46(2), pages 290-299, April.
    7. Yi Li & Lu Tian & Lee-Jen Wei, 2011. "Estimating Subject-Specific Dependent Competing Risk Profile with Censored Event Time Observations," Biometrics, The International Biometric Society, vol. 67(2), pages 427-435, June.
    8. Yeqian Liu & Tao Hu & Jianguo Sun, 2017. "Regression analysis of current status data in the presence of a cured subgroup and dependent censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 626-650, October.
    9. Muller, Ursula & Van Keilegom, Ingrid, 2016. "Goodness-of-t tests for the cure rate in a mixture cure model," LIDAM Discussion Papers ISBA 2016037, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    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:bla:jorssb:v:69:y:2007:i:3:p:285-306. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.