IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v27y2021i4d10.1007_s10985-021-09526-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-021-09526-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10985-021-09526-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. You-Gan Wang, 2003. "Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance," Biometrika, Biometrika Trust, vol. 90(1), pages 29-41, March.
    2. 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.
    3. 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.
    4. 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.
    5. 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, March.
    6. Terpstra, Jeff T. & McKean, Joseph W., 2005. "Rank-Based Analysis of Linear Models Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i07).
    7. Guo X. & Carlin B.P., 2004. "Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages," The American Statistician, American Statistical Association, vol. 58, pages 16-24, February.
    8. Sy Han Chiou & Sangwook Kang & Jun Yan, 2015. "Semiparametric Accelerated Failure Time Modeling for Clustered Failure Times From Stratified Sampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 621-629, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. Byungtae Seo & Sangwook Kang, 2023. "Accelerated failure time modeling via nonparametric mixtures," Biometrics, The International Biometric Society, vol. 79(1), pages 165-177, March.
    4. 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).
    5. 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.
    6. Kyu Hyun Kim & Daniel J. Caplan & Sangwook Kang, 2023. "Smoothed quantile regression for censored residual life," Computational Statistics, Springer, vol. 38(2), pages 1001-1022, June.
    7. Hong, Han & Mahajan, Aprajit & Nekipelov, Denis, 2015. "Extremum estimation and numerical derivatives," Journal of Econometrics, Elsevier, vol. 188(1), pages 250-263.
    8. You-Gan Wang & Yudong Zhao, 2008. "Weighted Rank Regression for Clustered Data Analysis," Biometrics, The International Biometric Society, vol. 64(1), pages 39-45, March.
    9. Li, Haifen & Zhang, Jiajia & Tang, Yincai, 2012. "Induced smoothing for the semiparametric accelerated hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4312-4319.
    10. Fu, Liya & Wang, You-Gan, 2016. "Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 492-502.
    11. Liya Fu & You-Gan Wang, 2012. "Efficient Estimation for Rank-Based Regression with Clustered Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1074-1082, December.
    12. Pang, Lei & Lu, Wenbin & Wang, Huixia Judy, 2012. "Variance estimation in censored quantile regression via induced smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 785-796.
    13. Zexi Cai & Tony Sit, 2023. "On interquantile smoothness of censored quantile regression with induced smoothing," Biometrics, The International Biometric Society, vol. 79(4), pages 3549-3563, December.
    14. Wenjing Yin & Sihai Dave Zhao & Feng Liang, 2022. "Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 282-318, April.
    15. Kangning Wang & Xiaofei Sun, 2020. "Efficient parameter estimation and variable selection in partial linear varying coefficient quantile regression model with longitudinal data," Statistical Papers, Springer, vol. 61(3), pages 967-995, June.
    16. Yize Zhao & Matthias Chung & Brent A. Johnson & Carlos S. Moreno & Qi Long, 2016. "Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1427-1439, October.
    17. Jing Lv & Chaohui Guo, 2017. "Efficient parameter estimation via modified Cholesky decomposition for quantile regression with longitudinal data," Computational Statistics, Springer, vol. 32(3), pages 947-975, September.
    18. Long Feng & Changliang Zou & Zhaojun Wang & Lixing Zhu, 2015. "Robust comparison of regression curves," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 185-204, March.
    19. Vens, Maren & Ziegler, Andreas, 2012. "Generalized estimating equations and regression diagnostics for longitudinal controlled clinical trials: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1232-1242.
    20. Xu, Linzhi & Zhang, Jiajia, 2010. "An EM-like algorithm for the semiparametric accelerated failure time gamma frailty model," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1467-1474, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lifeda:v:27:y:2021:i:4:d:10.1007_s10985-021-09526-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.