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Additive hazards model with auxiliary subgroup survival information

Author

Listed:
  • Jie He

    (Beijing Normal University)

  • Hui Li

    (Beijing Normal University)

  • Shumei Zhang

    (Beijing Normal University)

  • Xiaogang Duan

    (Beijing Normal University)

Abstract

The semiparametric additive hazards model is an important way for studying the effect of potential risk factors for right-censored time-to-event data. In this paper, we study the additive hazards model in the presence of auxiliary subgroup $$t^*$$ t ∗ -year survival information. We formulate the known auxiliary information in the form of estimating equations, and combine them with the conventional score-type estimating equations for the estimation of the regression parameters based on the maximum empirical likelihood method. We prove that the new estimator of the regression coefficients follows asymptotically a multivariate normal distribution with a sandwich-type covariance matrix that can be consistently estimated, and is strictly more efficient, in an asymptotic sense, than the conventional one without incorporation of the available auxiliary information. Simulation studies show that the new proposal has substantial advantages over the conventional one in terms of standard errors, and with the accommodation of more informative information, the proposed estimator becomes more competing. An AIDS data example is used for illustration.

Suggested Citation

  • Jie He & Hui Li & Shumei Zhang & Xiaogang Duan, 2019. "Additive hazards model with auxiliary subgroup survival information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 128-149, January.
    • Handle: RePEc:spr:lifeda:v:25:y:2019:i:1:d:10.1007_s10985-018-9426-7
    DOI: 10.1007/s10985-018-9426-7
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    References listed on IDEAS

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    1. Guosheng Yin & Jianwen Cai, 2004. "Additive hazards model with multivariate failure time data," Biometrika, Biometrika Trust, vol. 91(4), pages 801-818, December.
    2. Chiung-Yu Huang & Jing Qin & Huei-Ting Tsai, 2016. "Efficient Estimation of the Cox Model with Auxiliary Subgroup Survival Information," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 787-799, April.
    3. Nilanjan Chatterjee & Yi-Hau Chen & Paige Maas & Raymond J. Carroll, 2016. "Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 107-117, March.
    4. Hui Li & Xiaogang Duan & Guosheng Yin, 2016. "Generalized Method of Moments for Additive Hazards Model with Clustered Dental Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1124-1139, December.
    5. Jing Qin & Han Zhang & Pengfei Li & Demetrius Albanes & Kai Yu, 2015. "Using covariate-specific disease prevalence information to increase the power of case-control studies," Biometrika, Biometrika Trust, vol. 102(1), pages 169-180.
    6. Shang, Wenpeng & Wang, Xiao, 2017. "The generalized moment estimation of the additive–multiplicative hazard model with auxiliary survival information," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 154-169.
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    1. Ying Sheng & Yifei Sun & Detian Deng & Chiung‐Yu Huang, 2020. "Censored linear regression in the presence or absence of auxiliary survival information," Biometrics, The International Biometric Society, vol. 76(3), pages 734-745, September.

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