IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v36y2021i4d10.1007_s00180-021-01087-2.html
   My bibliography  Save this article

A computational method for estimating a change point in the Cox hazard model

Author

Listed:
  • G. Y. Arenas

    (Colegio de Postgraduados)

  • J. A. Villaseñor

    (Colegio de Postgraduados)

  • O. Palmeros

    (Universidad Autónoma de Chapingo)

  • F. Tajonar

    (Benemérita Universidad Autónoma de Puebla)

Abstract

The hazard function describes the instantaneous rate of failure at a time t, given that the individual survives up to the instant t. The effect of the covariates produces a variation in the hazard function, hence a change point might occur. When dealing with survival analysis, it is of interest to identify where a change point has occurred. This paper proposes a new method for estimating the change point in the Cox proportional hazard model, which is based on maximum likelihood estimation combined with moments estimation (ME) and a numerical mehtod to minimize an objective function given by ME. The mean square error of the estimator is obtained by Monte Carlo simulation, considering different scenarios. For the purpose of studying the behavior of the proposed estimator in terms of its mean square error, a comparative study against a known method to estimate the change point is included. A real data application is also included.

Suggested Citation

  • G. Y. Arenas & J. A. Villaseñor & O. Palmeros & F. Tajonar, 2021. "A computational method for estimating a change point in the Cox hazard model," Computational Statistics, Springer, vol. 36(4), pages 2491-2506, December.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:4:d:10.1007_s00180-021-01087-2
    DOI: 10.1007/s00180-021-01087-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-021-01087-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-021-01087-2?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.

    References listed on IDEAS

    as
    1. Victor Salinas & José Romeo & Alexis Peña, 2010. "On Bayesian estimation of a survival curve: comparative study and examples," Computational Statistics, Springer, vol. 25(3), pages 375-389, September.
    2. Oscar Palmeros & Jose A. Villaseñor & Elizabeth González, 2018. "On computing estimates of a change-point in the Weibull regression hazard model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(4), pages 642-648, March.
    3. Li, Yunxia & Qian, Lianfen & Zhang, Wei, 2013. "Estimation in a change-point hazard regression model with long-term survivors," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1683-1691.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alessandra R. Brazzale & Helmut Küchenhoff & Stefanie Krügel & Tobias S. Schiergens & Heiko Trentzsch & Wolfgang Hartl, 2019. "Nonparametric change point estimation for survival distributions with a partially constant hazard rate," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 301-321, April.

    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:spr:compst:v:36:y:2021:i:4:d:10.1007_s00180-021-01087-2. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.