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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

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.

Suggested Citation

  • Zhang, Jiajia & Peng, Yingwei & Li, Haifen, 2013. "A new semiparametric estimation method for accelerated hazards mixture cure model," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 95-102.
  • Handle: RePEc:eee:csdana:v:59:y:2013:i:c:p:95-102 DOI: 10.1016/j.csda.2012.09.017
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    References listed on IDEAS

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    1. Judy P. Sy & Jeremy M. G. Taylor, 2000. "Estimation in a Cox Proportional Hazards Cure Model," Biometrics, The International Biometric Society, vol. 56(1), pages 227-236, March.
    2. M. Jamshidian & R. I. Jennrich, 2000. "Standard errors for EM estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 257-270.
    3. Zeng, Donglin & Lin, D.Y., 2007. "Efficient Estimation for the Accelerated Failure Time Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1387-1396, December.
    4. Wenbin Lu, 2004. "On semiparametric transformation cure models," Biometrika, Biometrika Trust, vol. 91(2), pages 331-343, June.
    5. Jiajia Zhang & Yingwei Peng & Ou Zhao, 2011. "A New Semiparametric Estimation Method for Accelerated Hazard Model," Biometrics, The International Biometric Society, vol. 67(4), pages 1352-1360, December.
    6. Zhang, Jiajia & Peng, Yingwei, 2009. "Crossing hazard functions in common survival models," Statistics & Probability Letters, Elsevier, vol. 79(20), pages 2124-2130, October.
    7. Zhezhen Jin, 2003. "Rank-based inference for the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(2), pages 341-353, June.
    8. Yingwei Peng & Keith B. G. Dear, 2000. "A Nonparametric Mixture Model for Cure Rate Estimation," Biometrics, The International Biometric Society, vol. 56(1), pages 237-243, March.
    9. Ying Qing Chen, 2001. "Accelerated Hazards Regression Model and Its Adequacy for Censored Survival Data," Biometrics, The International Biometric Society, vol. 57(3), pages 853-860, September.
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    Cited by:

    1. Bremhorst, Vincent & Lambert, Philippe, 2016. "Flexible estimation in cure survival models using Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 270-284.
    2. López-Cheda, Ana & Cao, Ricardo & Jácome, M. Amalia & Van Keilegom, Ingrid, 2017. "Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 144-165.

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