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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
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    References listed on IDEAS

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    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.
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