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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
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    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.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-523M857-3/2/48b6c4e93520e0bcd788c89714344bb6
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 55 (2011)
    Issue (Month): 7 (July)
    Pages: 2334-2343

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    Handle: RePEc:eee:csdana:v:55:y:2011:i:7:p:2334-2343

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Clustered data Covariance matrix Gehan-type weight function Induced smoothing Rank estimation Survival data;

    References

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    1. Fu, Liya & Wang, You-Gan & Bai, Zhidong, 2010. "Rank regression for analysis of clustered data: A natural induced smoothing approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1036-1050, April.
    2. Peng, Limin & Fine, Jason P., 2006. "Rank Estimation of Accelerated Lifetime Models With Dependent Censoring," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1085-1093, September.
    3. Xuming He, 2002. "Estimation in a semiparametric model for longitudinal data with unspecified dependence structure," Biometrika, Biometrika Trust, vol. 89(3), pages 579-590, August.
    4. Wang, You-Gan & Shao, Quanxi & Zhu, Min, 2009. "Quantile regression without the curse of unsmoothness," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3696-3705, August.
    5. Zhezhen Jin, 2003. "Rank-based inference for the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(2), pages 341-353, June.
    6. B. M. Brown & You-Gan Wang, 2005. "Standard errors and covariance matrices for smoothed rank estimators," Biometrika, Biometrika Trust, vol. 92(1), pages 149-158, March.
    7. Z. Jin & D. Y. Lin & Z. Ying, 2006. "Rank Regression Analysis of Multivariate Failure Time Data Based on Marginal Linear Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 33(1), pages 1-23.
    8. Lynn M. Johnson & Robert L. Strawderman, 2009. "Induced smoothing for the semiparametric accelerated failure time model: asymptotics and extensions to clustered data," Biometrika, Biometrika Trust, vol. 96(3), pages 577-590.
    9. Heller, Glenn, 2007. "Smoothed Rank Regression With Censored Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 552-559, June.
    10. Zhezhen Jin & D. Y. Lin & Zhiliang Ying, 2006. "On least-squares regression with censored data," Biometrika, Biometrika Trust, vol. 93(1), pages 147-161, March.
    11. Heyde, C. C., 1987. "On combining quasi-likelihood estimating functions," Stochastic Processes and their Applications, Elsevier, vol. 25, pages 281-287.
    12. You-Gan Wang & Min Zhu, 2006. "Rank-based regression for analysis of repeated measures," Biometrika, Biometrika Trust, vol. 93(2), pages 459-464, June.
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