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Estimating the survival function based on the semi-Markov model for dependent censoring

Author

Listed:
  • Ziqiang Zhao

    (Fudan University)

  • Ming Zheng

    (Fudan University)

  • Zhezhen Jin

    (Columbia University)

Abstract

In this paper, we study a nonparametric maximum likelihood estimator (NPMLE) of the survival function based on a semi-Markov model under dependent censoring. We show that the NPMLE is asymptotically normal and achieves asymptotic nonparametric efficiency. We also provide a uniformly consistent estimator of the corresponding asymptotic covariance function based on an information operator. The finite-sample performance of the proposed NPMLE is examined with simulation studies, which show that the NPMLE has smaller mean squared error than the existing estimators and its corresponding pointwise confidence intervals have reasonable coverages. A real example is also presented.

Suggested Citation

  • Ziqiang Zhao & Ming Zheng & Zhezhen Jin, 2016. "Estimating the survival function based on the semi-Markov model for dependent censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(2), pages 161-190, April.
    • Handle: RePEc:spr:lifeda:v:22:y:2016:i:2:d:10.1007_s10985-015-9325-0
    DOI: 10.1007/s10985-015-9325-0
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    References listed on IDEAS

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    1. Somnath Datta & Glen A. Satten & Susmita Datta, 2000. "Nonparametric Estimation for the Three-Stage Irreversible Illness–Death Model," Biometrics, The International Biometric Society, vol. 56(3), pages 841-847, September.
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