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The generalized moment estimation of the additive–multiplicative hazard model with auxiliary survival information

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  • Shang, Wenpeng
  • Wang, Xiao

Abstract

Additive–multiplicative hazard model is a natural extension of the proportional hazard model and the additive hazard model in survival analysis. It is classical for applying the martingale estimating functions to estimate the regression parameters. However, the generalized moment method is employed to estimate the coefficients via synthesizing the auxiliary subgroup survival information. The estimators are established to be consistent and asymptotically normal. Furthermore, the method is more efficient than the famous martingale approach. In particular, these asymptotic variance–covariances are identical as the number of subgroups is equal to one. The large sample property of the Breslow estimator for the baseline cumulative hazard function is also investigated. Some extensive simulation studies are conducted to evaluate the finite-sample performances of the proposed method. A real data study is analyzed to show its practical utility.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:112:y:2017:i:c:p:154-169
    DOI: 10.1016/j.csda.2017.03.013
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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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