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A new semiparametric estimation method for accelerated hazards mixture cure model

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  • Zhang, Jiajia
  • Peng, Yingwei
  • Li, Haifen
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    Abstract

    The semiparametric accelerated hazards mixture cure model provides a useful alternative to analyze survival data with a cure fraction if covariates of interest have a gradual effect on the hazard of uncured patients. However, the application of the model may be hindered by the computational intractability of its estimation method due to non-smooth estimating equations involved. We propose a new semiparametric estimation method based on a smooth estimating equation for the model and demonstrate that the new method makes the parameter estimation more tractable without loss of efficiency. The proposed method is used to fit the model to a SEER breast cancer data set.

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 59 (2013)
    Issue (Month): C ()
    Pages: 95-102

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    Handle: RePEc:eee:csdana:v:59:y:2013:i:c:p:95-102

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: EM algorithm; Kernel-smoothed approximation; Non-smooth estimating equation; Profile likelihood;

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