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An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data

Author

Listed:
  • Liya Fu

    (Xi’an Jiaotong University)

  • Zhuoran Yang

    (Xi’an Jiaotong University)

  • Yan Zhou

    (Shenzhen University)

  • You-Gan Wang

    (Queensland University of Technology)

Abstract

In medical studies, the collected covariates contain underlying outliers. For clustered/longitudinal data with censored observations, the traditional Gehan-type estimator is robust to outliers in response but sensitive to outliers in the covariate domain, and it also ignores the within-cluster correlations. To take account of within-cluster correlations, varying cluster sizes, and outliers in covariates, we propose weighted Gehan-type estimating functions for parameter estimation in the accelerated failure time model for clustered data. We provide the asymptotic properties of the resulting estimators and carry out simulation studies to evaluate the performance of the proposed method under a variety of realistic settings. The simulation results demonstrate that the proposed method is robust to the outliers existing in the covariate domain and lead to much more efficient estimators when a strong within-cluster correlation exists. Finally, the proposed method is applied to two medical datasets and more reliable and convincing results are hence obtained.

Suggested Citation

  • Liya Fu & Zhuoran Yang & Yan Zhou & You-Gan Wang, 2021. "An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 679-709, October.
    • Handle: RePEc:spr:lifeda:v:27:y:2021:i:4:d:10.1007_s10985-021-09526-4
    DOI: 10.1007/s10985-021-09526-4
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    References listed on IDEAS

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