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On computing estimates of a change-point in the Weibull regression hazard model

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  • Oscar Palmeros
  • Jose A. Villaseñor
  • Elizabeth González

Abstract

The hazard function describes the instantaneous rate of failure at a time t, given that the individual survives up to t. In applications, the effect of covariates produce changes in the hazard function. When dealing with survival analysis, it is of interest to identify where a change point in time has occurred. In this work, covariates and censored variables are considered in order to estimate a change-point in the Weibull regression hazard model, which is a generalization of the exponential model. For this more general model, it is possible to obtain maximum likelihood estimators for the change-point and for the parameters involved. A Monte Carlo simulation study shows that indeed, it is possible to implement this model in practice. An application with clinical trial data coming from a treatment of chronic granulomatous disease is also included.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:642-648
    DOI: 10.1080/02664763.2017.1289366
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    Cited by:

    1. 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.

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