IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v103y2016icp79-90.html
   My bibliography  Save this article

Cox regression analysis of dependent interval-censored failure time data

Author

Listed:
  • Ma, Ling
  • Hu, Tao
  • Sun, Jianguo

Abstract

Many procedures have been proposed for regression analysis of interval-censored failure time data arising from the Cox or proportional hazards model. However, most of these existing methods only apply to the situation where the censoring mechanism generating censoring intervals is independent of the failure time of interest, and it is well-known that sometimes this may not be true in practice. To address this issue, a new approach that allows the dependence between the censoring mechanism and the failure time is proposed. More specifically, a situation where the dependence is through the length of censoring intervals is considered as it is often the case in follow-up studies. The asymptotic properties of the proposed estimators are established and the numerical studies are conducted for the assessment of the finite sample properties of the estimators.

Suggested Citation

  • Ma, Ling & Hu, Tao & Sun, Jianguo, 2016. "Cox regression analysis of dependent interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 79-90.
    • Handle: RePEc:eee:csdana:v:103:y:2016:i:c:p:79-90
    DOI: 10.1016/j.csda.2016.04.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947316300901
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2016.04.011?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. Yi‐Hau Chen, 2010. "Semiparametric marginal regression analysis for dependent competing risks under an assumed copula," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 235-251, March.
    2. 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.
    3. Chen, Xiaohong & Fan, Yanqin & Tsyrennikov, Viktor, 2006. "Efficient Estimation of Semiparametric Multivariate Copula Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1228-1240, September.
    4. Minggen Lu & Ying Zhang & Jian Huang, 2007. "Estimation of the mean function with panel count data using monotone polynomial splines," Biometrika, Biometrika Trust, vol. 94(3), pages 705-718.
    5. Anton Schick & Qiqing Yu, 2000. "Consistency of the GMLE with Mixed Case Interval‐Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(1), pages 45-55, March.
    6. Stephen W. Lagakos & Thomas A. Louis, 1988. "Use of Tumour Lethality to Interpret Tumorigenicity Experiments Lacking Cause‐Of‐Death Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 37(2), pages 169-179, June.
    7. Klara Goethals & Paul Janssen & Luc Duchateau, 2008. "Frailty models and copulas: similarities and differences," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(9), pages 1071-1079.
    8. 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.
    9. Ying Zhang & Lei Hua & Jian Huang, 2010. "A Spline‐Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval‐Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 338-354, June.
    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. Fábio Prataviera & Elizabeth M. Hashimoto & Edwin M. M. Ortega & Taciana V. Savian & Gauss M. Cordeiro, 2023. "Interval-Censored Regression with Non-Proportional Hazards with Applications," Stats, MDPI, vol. 6(2), pages 1-14, May.
    2. Xifen Huang & Jinfeng Xu, 2022. "Subgroup Identification and Regression Analysis of Clustered and Heterogeneous Interval-Censored Data," Mathematics, MDPI, vol. 10(6), pages 1-11, March.
    3. Yeqian Liu & Tao Hu & Jianguo Sun, 2017. "Regression analysis of current status data in the presence of a cured subgroup and dependent censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 626-650, October.

    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. Chen, Xuerong & Hu, Tao & Sun, Jianguo, 2017. "Sieve maximum likelihood estimation for the proportional hazards model under informative censoring," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 224-234.
    2. 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.
    3. Zhiguo Li & Kouros Owzar, 2016. "Fitting Cox Models with Doubly Censored Data Using Spline-Based Sieve Marginal Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 476-486, June.
    4. Minggen Lu, 2015. "Spline estimation of generalised monotonic regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(1), pages 19-39, March.
    5. 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.
    6. 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.
    7. Yuan Wu & Christina D. Chambers & Ronghui Xu, 2019. "Semiparametric sieve maximum likelihood estimation under cure model with partly interval censored and left truncated data for application to spontaneous abortion," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 507-528, July.
    8. Cheng, Guang & Zhou, Lan & Chen, Xiaohong & Huang, Jianhua Z., 2014. "Efficient estimation of semiparametric copula models for bivariate survival data," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 330-344.
    9. Li, Chenxi, 2016. "The Fine–Gray model under interval censored competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 327-344.
    10. Liu, Xiaoyu & Xiang, Liming, 2021. "Generalized accelerated hazards mixture cure models with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    11. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    12. Minggen Lu & Dana Loomis, 2013. "Spline-based semiparametric estimation of partially linear Poisson regression with single-index models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(4), pages 905-922, December.
    13. Qingning Zhou & Jianwen Cai & Haibo Zhou, 2018. "Outcome†dependent sampling with interval†censored failure time data," Biometrics, The International Biometric Society, vol. 74(1), pages 58-67, March.
    14. Ye, Mao & Lu, Zhao-Hua & Li, Yimei & Song, Xinyuan, 2019. "Finite mixture of varying coefficient model: Estimation and component selection," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 452-474.
    15. Agbeyegbe, Terence D., 2015. "An inverted U-shaped crude oil price return-implied volatility relationship," Review of Financial Economics, Elsevier, vol. 27(C), pages 28-45.
    16. Giovanni Compiani & Philip Haile & Marcelo Sant’Anna, 2020. "Common Values, Unobserved Heterogeneity, and Endogenous Entry in US Offshore Oil Lease Auctions," Journal of Political Economy, University of Chicago Press, vol. 128(10), pages 3872-3912.
    17. Eddie Anderson & Artem Prokhorov & Yajing Zhu, 2020. "A Simple Estimator of Two‐Dimensional Copulas, with Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1375-1412, December.
    18. Lo Simon M.S. & Wilke Ralf A., 2014. "A Regression Model for the Copula-Graphic Estimator," Journal of Econometric Methods, De Gruyter, vol. 3(1), pages 21-46, January.
    19. Thomas, Alban & Chakir, Raja, 2020. "Unintended consequences of environmental policies: the case of set-aside and agricultural intensification," TSE Working Papers 20-1066, Toulouse School of Economics (TSE).
    20. Jeffrey Racine, 2015. "Mixed data kernel copulas," Empirical Economics, Springer, vol. 48(1), pages 37-59, February.

    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:103:y:2016:i:c:p:79-90. 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.