IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v144y2020ics0167947319302464.html
   My bibliography  Save this article

Estimation of the additive hazards model with case K interval-censored failure time data in the presence of informative censoring

Author

Listed:
  • Wang, Shuying
  • Wang, Chunjie
  • Wang, Peijie
  • Sun, Jianguo

Abstract

The additive hazards model is one of the most commonly used model in regression analysis of failure time data and many estimation procedures have been developed for its inference under various situations (Kalbfleisch and Prentice (2002); Lin and Ying (1994); Sun (2006)). In this paper, we consider a situation, case K interval-censored data with informative interval censoring, that often occurs in practice such as medical follow-up studies but has not been discussed much in the literature due to the difficulties involved. For the problem, a joint model is proposed to describe the correlation between the failure time of interest and the underlying censoring or observation process and a sieve maximum likelihood approach is developed. In particular, an EM algorithm is presented for the implementation of the proposed estimation procedure and the asymptotic properties of the resulting estimators are established. A simulation study is conducted to assess the finite sample performance of the proposed method and suggests that it works well for practical situations. Also the method is applied to an AIDS study that motivated this study.

Suggested Citation

  • Wang, Shuying & Wang, Chunjie & Wang, Peijie & Sun, Jianguo, 2020. "Estimation of the additive hazards model with case K interval-censored failure time data in the presence of informative censoring," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:csdana:v:144:y:2020:i:c:s0167947319302464
    DOI: 10.1016/j.csda.2019.106891
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947319302464
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2019.106891?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. C.-Y. Huang & J. Qin & M.-C. Wang, 2010. "Semiparametric Analysis for Recurrent Event Data with Time-Dependent Covariates and Informative Censoring," Biometrics, The International Biometric Society, vol. 66(1), pages 39-49, March.
    2. Peijie Wang & Hui Zhao & Jianguo Sun, 2016. "Regression analysis of case K interval‐censored failure time data in the presence of informative censoring," Biometrics, The International Biometric Society, vol. 72(4), pages 1103-1112, December.
    3. Ling Ma & Tao Hu & Jianguo Sun, 2015. "Sieve maximum likelihood regression analysis of dependent current status data," Biometrika, Biometrika Trust, vol. 102(3), pages 731-738.
    4. Li, Shuwei & Hu, Tao & Wang, Peijie & Sun, Jianguo, 2017. "Regression analysis of current status data in the presence of dependent censoring with applications to tumorigenicity experiments," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 75-86.
    5. Liang Zhu & Hui Zhao & Jianguo Sun & Wendy Leisenring & Leslie L. Robison, 2015. "Regression analysis of mixed recurrent-event and panel-count data with additive rate models," Biometrics, The International Biometric Society, vol. 71(1), pages 71-79, March.
    6. Dianne M. Finkelstein & William B. Goggins & David A. Schoenfeld, 2002. "Analysis of Failure Time Data with Dependent Interval Censoring," Biometrics, The International Biometric Society, vol. 58(2), pages 298-304, June.
    7. Wang, Shuying & Wang, Chunjie & Wang, Peijie & Sun, Jianguo, 2018. "Semiparametric analysis of the additive hazards model with informatively interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 1-9.
    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. Shuying Wang & Chunjie Wang & Jianguo Sun, 2021. "An additive hazards cure model with informative interval censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 244-268, April.

    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. Shuying Wang & Chunjie Wang & Jianguo Sun, 2021. "An additive hazards cure model with informative interval censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 244-268, April.
    2. Mengzhu Yu & Mingyue Du, 2022. "Regression Analysis of Multivariate Interval-Censored Failure Time Data under Transformation Model with Informative Censoring," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    3. Wang, Shuying & Wang, Chunjie & Wang, Peijie & Sun, Jianguo, 2018. "Semiparametric analysis of the additive hazards model with informatively interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 1-9.
    4. Ruiwen Zhou & Huiqiong Li & Jianguo Sun & Niansheng Tang, 2022. "A new approach to estimation of the proportional hazards model based on interval-censored data with missing covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 335-355, July.
    5. Tianyi Lu & Shuwei Li & Liuquan Sun, 2023. "Combined estimating equation approaches for the additive hazards model with left-truncated and interval-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 672-697, July.
    6. Fei Gao & Donglin Zeng & Dan‐Yu Lin, 2018. "Semiparametric regression analysis of interval‐censored data with informative dropout," Biometrics, The International Biometric Society, vol. 74(4), pages 1213-1222, December.
    7. Xiaoyu Wang & Liuquan Sun, 2023. "Joint modeling of generalized scale-change models for recurrent event and failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 1-33, January.
    8. Peijie Wang & Hui Zhao & Jianguo Sun, 2016. "Regression analysis of case K interval‐censored failure time data in the presence of informative censoring," Biometrics, The International Biometric Society, vol. 72(4), pages 1103-1112, December.
    9. Pantazis, Nikos & Kenward, Michael G. & Touloumi, Giota, 2013. "Performance of parametric survival models under non-random interval censoring: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 16-30.
    10. Dongdong Li & X. Joan Hu & Mary L. McBride & John J. Spinelli, 2020. "Multiple event times in the presence of informative censoring: modeling and analysis by copulas," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 573-602, July.
    11. Da Xu & Hui Zhao & Jianguo Sun, 2018. "Joint analysis of interval-censored failure time data and panel count data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 94-109, January.
    12. Jinyang Wang & Piao Chen & Zhisheng Ye, 2022. "Efficient semiparametric estimation of time‐censored intensity‐reduction models for repairable systems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1860-1888, December.
    13. Liu, Li & Xiang, Liming, 2019. "Missing covariate data in generalized linear mixed models with distribution-free random effects," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 1-16.
    14. Rebecca A. Betensky & Dianne M. Finkelstein, 2002. "Testing for Dependence Between Failure Time and Visit Compliance with Interval-Censored Data," Biometrics, The International Biometric Society, vol. 58(1), pages 58-63, March.
    15. Ryan Sun & Dayu Sun & Liang Zhu & Jianguo Sun, 2023. "Regression analysis of general mixed recurrent event data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 807-822, October.
    16. Mengyue Zhang & Shishun Zhao & Tao Hu & Da Xu & Jianguo Sun, 2023. "Regression Analysis of Dependent Current Status Data with Left Truncation," Mathematics, MDPI, vol. 11(16), pages 1-13, August.
    17. Yayuan Zhu & Ziqi Chen & Jerald F. Lawless, 2022. "Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 236-264, March.
    18. Dianne M. Finkelstein & Rui Wang & Linda H. Ficociello & David A. Schoenfeld, 2010. "A Score Test for Association of a Longitudinal Marker and an Event with Missing Data," Biometrics, The International Biometric Society, vol. 66(3), pages 726-732, September.
    19. Yimei Li & Liang Zhu & Lei Liu & Leslie L. Robison, 2021. "Regression Analysis of Mixed Panel-Count Data with Application to Cancer Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 178-195, April.
    20. Wittkowski, Knut M., 2003. "Novel Methods for Multivariate Ordinal Data applied to Genetic Diplotypes, Genomic Pathways, Risk Profiles, and Pattern Similarity," MPRA Paper 4570, University Library of Munich, Germany.

    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:eee:csdana:v:144:y:2020:i:c:s0167947319302464. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.