IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v23y2017i4d10.1007_s10985-016-9384-x.html
   My bibliography  Save this article

Regression analysis of clustered failure time data with informative cluster size under the additive transformation models

Author

Listed:
  • Ling Chen

    (Washington University School of Medicine)

  • Yanqin Feng

    (Wuhan University
    Wuhan University)

  • Jianguo Sun

    (University of Missouri)

Abstract

This paper discusses regression analysis of clustered failure time data, which occur when the failure times of interest are collected from clusters. In particular, we consider the situation where the correlated failure times of interest may be related to cluster sizes. For inference, we present two estimation procedures, the weighted estimating equation-based method and the within-cluster resampling-based method, when the correlated failure times of interest arise from a class of additive transformation models. The former makes use of the inverse of cluster sizes as weights in the estimating equations, while the latter can be easily implemented by using the existing software packages for right-censored failure time data. An extensive simulation study is conducted and indicates that the proposed approaches work well in both the situations with and without informative cluster size. They are applied to a dental study that motivated this study.

Suggested Citation

  • Ling Chen & Yanqin Feng & Jianguo Sun, 2017. "Regression analysis of clustered failure time data with informative cluster size under the additive transformation models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 651-670, October.
    • Handle: RePEc:spr:lifeda:v:23:y:2017:i:4:d:10.1007_s10985-016-9384-x
    DOI: 10.1007/s10985-016-9384-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-016-9384-x
    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/s10985-016-9384-x?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. Xiuyu J. Cong & Guosheng Yin & Yu Shen, 2007. "Marginal Analysis of Correlated Failure Time Data with Informative Cluster Sizes," Biometrics, The International Biometric Society, vol. 63(3), pages 663-672, September.
    2. Guosheng Yin & Jianwen Cai, 2004. "Additive hazards model with multivariate failure time data," Biometrika, Biometrika Trust, vol. 91(4), pages 801-818, December.
    3. David B. Dunson & Zhen Chen & Jean Harry, 2003. "A Bayesian Approach for Joint Modeling of Cluster Size and Subunit-Specific Outcomes," Biometrics, The International Biometric Society, vol. 59(3), pages 521-530, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhongwen Zhang & Xiaoguang Wang & Yingwei Peng, 2022. "An additive hazards frailty model with semi-varying coefficients," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 116-138, January.

    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. Chun Yin Lee & Kin Yau Wong & Kwok Fai Lam & Dipankar Bandyopadhyay, 2023. "A semiparametric joint model for cluster size and subunit‐specific interval‐censored outcomes," Biometrics, The International Biometric Society, vol. 79(3), pages 2010-2022, September.
    2. Ling Lan & Dipankar Bandyopadhyay & Somnath Datta, 2017. "Non-parametric regression in clustered multistate current status data with informative cluster size," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(1), pages 31-57, January.
    3. Zhang, Xinyan & Sun, Jianguo, 2010. "Regression analysis of clustered interval-censored failure time data with informative cluster size," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1817-1823, July.
    4. Jaakko Nevalainen & Somnath Datta & Hannu Oja, 2014. "Inference on the marginal distribution of clustered data with informative cluster size," Statistical Papers, Springer, vol. 55(1), pages 71-92, February.
    5. Jie He & Hui Li & Shumei Zhang & Xiaogang Duan, 2019. "Additive hazards model with auxiliary subgroup survival information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 128-149, January.
    6. Hui Li & Xiaogang Duan & Guosheng Yin, 2016. "Generalized Method of Moments for Additive Hazards Model with Clustered Dental Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1124-1139, December.
    7. Kassandra Fronczyk & Athanasios Kottas, 2017. "Risk Assessment for Toxicity Experiments with Discrete and Continuous Outcomes: A Bayesian Nonparametric Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 585-601, December.
    8. Xiaoyun Li & Dipankar Bandyopadhyay & Stuart Lipsitz & Debajyoti Sinha, 2011. "Likelihood Methods for Binary Responses of Present Components in a Cluster," Biometrics, The International Biometric Society, vol. 67(2), pages 629-635, June.
    9. Shaun R. Seaman & Menelaos Pavlou & Andrew J. Copas, 2014. "Methods for observed-cluster inference when cluster size is informative: A review and clarifications," Biometrics, The International Biometric Society, vol. 70(2), pages 449-456, June.
    10. Matthew W. Wheeler & A. John Bailer, 2009. "Benchmark Dose Estimation Incorporating Multiple Data Sources," Risk Analysis, John Wiley & Sons, vol. 29(2), pages 249-256, February.
    11. Reem Aljarallah & Samer A Kharroubi, 2021. "Use of Bayesian Markov Chain Monte Carlo Methods to Model Kuwait Medical Genetic Center Data: An Application to Down Syndrome and Mental Retardation," Mathematics, MDPI, vol. 9(3), pages 1-11, January.
    12. Claudia Czado & Anette Heyn & Gernot Müller, 2011. "Modeling individual migraine severity with autoregressive ordered probit models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 101-121, March.
    13. Julie S. Najita & Yi Li & Paul J. Catalano, 2009. "A novel application of a bivariate regression model for binary and continuous outcomes to studies of fetal toxicity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 555-573, September.
    14. Zhang Xinyan & Sun Jianguo, 2013. "Semiparametric Regression Analysis of Clustered Interval-Censored Failure Time Data with Informative Cluster Size," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 205-214, August.
    15. Jieli Ding & Liuquan Sun, 2017. "Additive mixed effect model for recurrent gap time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 223-253, April.
    16. Sean M. O'Brien & David B. Dunson, 2004. "Bayesian Multivariate Logistic Regression," Biometrics, The International Biometric Society, vol. 60(3), pages 739-746, September.
    17. Zhen Pang & Anthony Y. C. Kuk, 2007. "Test of Marginal Compatibility and Smoothing Methods for Exchangeable Binary Data with Unequal Cluster Sizes," Biometrics, The International Biometric Society, vol. 63(1), pages 218-227, March.
    18. Teng Fei & John Hanfelt & Limin Peng, 2023. "Evaluating the association between latent classes and competing risks outcomes with multiphenotype data," Biometrics, The International Biometric Society, vol. 79(1), pages 488-501, March.
    19. Shuling Liu & Amita K. Manatunga & Limin Peng & Michele Marcus, 2017. "A joint modeling approach for multivariate survival data with random length," Biometrics, The International Biometric Society, vol. 73(2), pages 666-677, June.
    20. Julie S. Najita & Paul J. Catalano, 2013. "On Determining the BMD from Multiple Outcomes in Developmental Toxicity Studies when One Outcome is Intentionally Missing," Risk Analysis, John Wiley & Sons, vol. 33(8), pages 1500-1509, August.

    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:23:y:2017:i:4:d:10.1007_s10985-016-9384-x. 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.