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Induced smoothing for the semiparametric accelerated hazards model

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  • Li, Haifen
  • Zhang, Jiajia
  • Tang, Yincai

Abstract

Compared to the proportional hazards model and accelerated failure time model, the accelerated hazards model has a unique property in its application, in that it can allow gradual effects of the treatment. However, its application is still very limited, partly due to the complexity of existing semiparametric estimation methods. We propose a new semiparametric estimation method based on the induced smoothing and rank type estimates. The parameter estimates and their variances can be easily obtained from the smoothed estimating equation; thus it is easy to use in practice. Our numerical study shows that the new method is more efficient than the existing methods with respect to its variance estimation and coverage probability. The proposed method is employed to reanalyze a data set from a brain tumor treatment study.

Suggested Citation

  • Li, Haifen & Zhang, Jiajia & Tang, Yincai, 2012. "Induced smoothing for the semiparametric accelerated hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4312-4319.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:12:p:4312-4319
    DOI: 10.1016/j.csda.2012.04.001
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    References listed on IDEAS

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    1. Pang, Lei & Lu, Wenbin & Wang, Huixia Judy, 2012. "Variance estimation in censored quantile regression via induced smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 785-796.
    2. 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.
    3. B. M. Brown & You-Gan Wang, 2005. "Standard errors and covariance matrices for smoothed rank estimators," Biometrika, Biometrika Trust, vol. 92(1), pages 149-158, March.
    4. 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.
    5. Lynn M. Johnson & Robert L. Strawderman, 2009. "Induced smoothing for the semiparametric accelerated failure time model: asymptotics and extensions to clustered data," Biometrika, Biometrika Trust, vol. 96(3), pages 577-590.
    6. Fu, Liya & Wang, You-Gan & Bai, Zhidong, 2010. "Rank regression for analysis of clustered data: A natural induced smoothing approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1036-1050, April.
    7. Zhang, Jiajia & Peng, Yingwei, 2009. "Crossing hazard functions in common survival models," Statistics & Probability Letters, Elsevier, vol. 79(20), pages 2124-2130, October.
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