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Rank regression for accelerated failure time model with clustered and censored data

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  • Wang, You-Gan
  • Fu, Liya

Abstract

For clustered survival data, the traditional Gehan-type estimator is asymptotically equivalent to using only the between-cluster ranks, and the within-cluster ranks are ignored. The contribution of this paper is two fold, (i) incorporating within-cluster ranks in censored data analysis, and (ii) applying the induced smoothing of Brown and Wang (2005, Biometrika) for computational convenience. Asymptotic properties of the resulting estimating functions are given. We also carry out numerical studies to assess the performance of the proposed approach and conclude that the proposed approach can lead to much improved estimators when strong clustering effects exist. A dataset from a litter-matched tumorigenesis experiment is used for illustration.

Suggested Citation

  • Wang, You-Gan & Fu, Liya, 2011. "Rank regression for accelerated failure time model with clustered and censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2334-2343, July.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:7:p:2334-2343
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    References listed on IDEAS

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    Cited by:

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    2. Chiou, Sy Han & Kang, Sangwook & Yan, Jun, 2014. "Fitting Accelerated Failure Time Models in Routine Survival Analysis with R Package aftgee," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i11).
    3. 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.
    4. Xue Yu & Yichuan Zhao, 2019. "Jackknife empirical likelihood inference for the accelerated failure time model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 269-288, March.

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