IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i1p78-89.html
   My bibliography  Save this article

Confidence intervals for median survival time with recurrent event data

Author

Listed:
  • Gonzalez, Juan R.
  • Peña, Edsel A.
  • Delicado, Pedro

Abstract

Several methods of constructing confidence intervals for the median survival time of a recurrent event data are developed. One of them is based on asymptotic variances estimated using some transformations. Others are based on bootstrap techniques. Two types of recurrent event models are considered: the first one is a model where the inter-event times are independent and identically distributed, and the second one is a model where the inter-event times are associated, with the association arising from a gamma frailty model. Bootstrap and asymptotic confidence intervals are studied through simulation. These methods are applied and compared using two real data sets arising in the biomedical and public health settings, using an available R package. The first example belongs to data from a study concerning small bowel motility where an independent model may be assumed. The second example involves hospital readmissions in patients diagnosed with colorectal cancer. In this example the interoccurrence times are correlated.

Suggested Citation

  • Gonzalez, Juan R. & Peña, Edsel A. & Delicado, Pedro, 2010. "Confidence intervals for median survival time with recurrent event data," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 78-89, January.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:78-89
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00258-8
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Chiung-Yu Huang & Mei-Cheng Wang, 2004. "Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1153-1165, December.
    2. Pena E.A. & Strawderman R.L. & Hollander M., 2001. "Nonparametric Estimation With Recurrent Event Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1299-1315, December.
    3. Frobish, Daniel & Ebrahimi, Nader, 2009. "Parametric estimation of change-points for actual event data in recurrent events models," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 671-682, January.
    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. de Peretti, Christian & Siani, Carole, 2010. "Graphical methods for investigating the finite-sample properties of confidence regions," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 262-271, February.

    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. Russell T. Shinohara & Yifei Sun & Mei-Cheng Wang, 2018. "Alternating event processes during lifetimes: population dynamics and statistical inference," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 110-125, January.
    2. Xiaowei Sun & Jieli Ding & Liuquan Sun, 2020. "A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 471-492, July.
    3. Jin-Jian Hsieh & A. Adam Ding & Weijing Wang, 2011. "Regression Analysis for Recurrent Events Data under Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(3), pages 719-729, September.
    4. 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.
    5. 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.
    6. Qing Pan & Douglas E. Schaubel, 2009. "Flexible Estimation of Differences in Treatment-Specific Recurrent Event Means in the Presence of a Terminating Event," Biometrics, The International Biometric Society, vol. 65(3), pages 753-761, September.
    7. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.
    8. Tianmeng Lyu & Björn Bornkamp & Guenther Mueller‐Velten & Heinz Schmidli, 2023. "Bayesian inference for a principal stratum estimand on recurrent events truncated by death," Biometrics, The International Biometric Society, vol. 79(4), pages 3792-3802, December.
    9. 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.
    10. Jianguo Sun & Xingwei Tong & Xin He, 2007. "Regression Analysis of Panel Count Data with Dependent Observation Times," Biometrics, The International Biometric Society, vol. 63(4), pages 1053-1059, December.
    11. Kwun Chuen Gary Chan & Mei-Cheng Wang, 2017. "Semiparametric Modeling and Estimation of the Terminal Behavior of Recurrent Marker Processes Before Failure Events," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 351-362, January.
    12. Dongxiao Han & Xiaogang Su & Liuquan Sun & Zhou Zhang & Lei Liu, 2020. "Variable selection in joint frailty models of recurrent and terminal events," Biometrics, The International Biometric Society, vol. 76(4), pages 1330-1339, December.
    13. Bhupendra Singh & Shubhi Rathi & Gajraj Singh & Puneet Kumar Gupta, 2022. "A change-time hazard rate model and its goodness of fit," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1903-1912, August.
    14. Shen, Pao-sheng, 2015. "The inverse probability weighted estimators for distribution functions of the bivariate recurrent events," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 91-99.
    15. Gongjun Xu & Sy Han Chiou & Chiung-Yu Huang & Mei-Cheng Wang & Jun Yan, 2017. "Joint Scale-Change Models for Recurrent Events and Failure Time," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 794-805, April.
    16. Hong Zhu, 2014. "Non-parametric Analysis of Gap Times for Multiple Event Data: An Overview," International Statistical Review, International Statistical Institute, vol. 82(1), pages 106-122, April.
    17. Laura M. Yee & Kwun Chuen Gary Chan, 2017. "Nonparametric inference for the joint distribution of recurrent marked variables and recurrent survival time," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 207-222, April.
    18. Ding, A. Adam, 2010. "Identifiability conditions for covariate effects model on survival times under informative censoring," Statistics & Probability Letters, Elsevier, vol. 80(11-12), pages 911-915, June.
    19. Zhao, Xiaobing & Zhou, Xian, 2012. "Copula models for insurance claim numbers with excess zeros and time-dependence," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 191-199.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:54:y:2010:i:1:p:78-89. 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.