IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v22y2016i2d10.1007_s10985-015-9327-y.html
   My bibliography  Save this article

Time-varying coefficients in a multivariate frailty model: Application to breast cancer recurrences of several types and death

Author

Listed:
  • Yassin Mazroui

    (Sorbonne Universités, UPMC Univ Paris 06
    INSERM, UMR_S 1136
    Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136)

  • Audrey Mauguen

    (Université Bordeaux Segalen)

  • Simone Mathoulin-Pélissier

    (INSERM CIC-EC7, ISPED, Centre INSERM U-897)

  • Gaetan MacGrogan

    (Institut Bergonié)

  • Véronique Brouste

    (Institut Bergonié)

  • Virginie Rondeau

    (Université Bordeaux Segalen)

Abstract

During their follow-up, patients with cancer can experience several types of recurrent events and can also die. Over the last decades, several joint models have been proposed to deal with recurrent events with dependent terminal event. Most of them require the proportional hazard assumption. In the case of long follow-up, this assumption could be violated. We propose a joint frailty model for two types of recurrent events and a dependent terminal event to account for potential dependencies between events with potentially time-varying coefficients. For that, regression splines are used to model the time-varying coefficients. Baseline hazard functions (BHF) are estimated with piecewise constant functions or with cubic M-Splines functions. The maximum likelihood estimation method provides parameter estimates. Likelihood ratio tests are performed to test the time dependency and the statistical association of the covariates. This model was driven by breast cancer data where the maximum follow-up was close to 20 years.

Suggested Citation

  • Yassin Mazroui & Audrey Mauguen & Simone Mathoulin-Pélissier & Gaetan MacGrogan & Véronique Brouste & Virginie Rondeau, 2016. "Time-varying coefficients in a multivariate frailty model: Application to breast cancer recurrences of several types and death," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(2), pages 191-215, April.
  • Handle: RePEc:spr:lifeda:v:22:y:2016:i:2:d:10.1007_s10985-015-9327-y
    DOI: 10.1007/s10985-015-9327-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-015-9327-y
    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/s10985-015-9327-y?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. Zhangsheng Yu & Xihong Lin, 2008. "Nonparametric regression using local kernel estimating equations for correlated failure time data," Biometrika, Biometrika Trust, vol. 95(1), pages 123-137.
    2. Donglin Zeng & D. Y. Lin, 2009. "Semiparametric Transformation Models with Random Effects for Joint Analysis of Recurrent and Terminal Events," Biometrics, The International Biometric Society, vol. 65(3), pages 746-752, September.
    3. Yining Ye & John D. Kalbfleisch & Douglas E. Schaubel, 2007. "Semiparametric Analysis of Correlated Recurrent and Terminal Events," Biometrics, The International Biometric Society, vol. 63(1), pages 78-87, March.
    4. Debashis Ghosh & D. Y. Lin, 2000. "Nonparametric Analysis of Recurrent Events and Death," Biometrics, The International Biometric Society, vol. 56(2), pages 554-562, June.
    5. 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.
    6. Lei Liu & Robert A. Wolfe & Xuelin Huang, 2004. "Shared Frailty Models for Recurrent Events and a Terminal Event," Biometrics, The International Biometric Society, vol. 60(3), pages 747-756, September.
    7. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    8. Göran Kauermann & Tatyana Krivobokova & Ludwig Fahrmeir, 2009. "Some asymptotic results on generalized penalized spline smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 487-503, April.
    9. Torben Martinussen & Thomas H. Scheike & Ib M. Skovgaard, 2002. "Efficient Estimation of Fixed and Time‐varying Covariate Effects in Multiplicative Intensity Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 57-74, March.
    10. Donglin Zeng & Jianwen Cai, 2010. "A semiparametric additive rate model for recurrent events with an informative terminal event," Biometrika, Biometrika Trust, vol. 97(3), pages 699-712.
    11. Zongwu Cai & Yanqing Sun, 2003. "Local Linear Estimation for Time‐Dependent Coefficients in Cox's Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 93-111, March.
    12. Wang M-C. & Qin J. & Chiang C-T., 2001. "Analyzing Recurrent Event Data With Informative Censoring," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1057-1065, September.
    13. Luc Duchateau & Paul Janssen & Iva Kezic & Catherine Fortpied, 2003. "Evolution of recurrent asthma event rate over time in frailty models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(3), pages 355-363, July.
    14. Xuelin Huang & Lei Liu, 2007. "A Joint Frailty Model for Survival and Gap Times Between Recurrent Events," Biometrics, The International Biometric Society, vol. 63(2), pages 389-397, June.
    15. Rondeau, Virginie & Marzroui, Yassin & Gonzalez, Juan R., 2012. "frailtypack: An R Package for the Analysis of Correlated Survival Data with Frailty Models Using Penalized Likelihood Estimation or Parametrical Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i04).
    16. Chin-Tsang Chiang & Mei-Cheng Wang, 2009. "Varying-coefficient model for the occurrence rate function of recurrent events," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 197-213, March.
    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. Ying Hung & Li‐Hsiang Lin & C. F. Jeff Wu, 2022. "Varying coefficient frailty models with applications in single molecular experiments," Biometrics, The International Biometric Society, vol. 78(2), pages 474-486, June.
    2. Lawrence Kryzanowski & Yanting Wu, 2023. "Signaling effects of recurrent list‐price reductions on the likelihood of house sales," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 99-130, 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. 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.
    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. Xiaoyu Che & John Angus, 2016. "A new joint model of recurrent event data with the additive hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(7), pages 763-787, October.
    4. 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.
    5. 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.
    6. Tianyu Zhan & Douglas E. Schaubel, 2019. "Semiparametric temporal process regression of survival-out-of-hospital," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 322-340, April.
    7. P. G. Sankaran & P. Anisha, 2011. "Shared frailty model for recurrent event data with multiple causes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2859-2868, February.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Jie Zhou & Haixiang Zhang & Liuquan Sun & Jianguo Sun, 2017. "Joint analysis of panel count data with an informative observation process and a dependent terminal event," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 560-584, October.
    14. Xingqiu Zhao & Jie Zhou & Liuquan Sun, 2011. "Semiparametric Transformation Models with Time-Varying Coefficients for Recurrent and Terminal Events," Biometrics, The International Biometric Society, vol. 67(2), pages 404-414, June.
    15. Yifei Sun & Mei-Cheng Wang, 2017. "Evaluating Utility Measurement From Recurrent Marker Processes in the Presence of Competing Terminal Events," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 745-756, April.
    16. Hui Zhao & Yang Li & Jianguo Sun, 2013. "Semiparametric analysis of multivariate panel count data with dependent observation processes and a terminal event," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 379-394, June.
    17. Qing Cai & Mei‐Cheng Wang & Kwun Chuen Gary Chan, 2017. "Joint modeling of longitudinal, recurrent events and failure time data for survivor's population," Biometrics, The International Biometric Society, vol. 73(4), pages 1150-1160, December.
    18. 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.
    19. John D. Kalbfleisch & Douglas E. Schaubel & Yining Ye & Qi Gong, 2013. "An Estimating Function Approach to the Analysis of Recurrent and Terminal Events," Biometrics, The International Biometric Society, vol. 69(2), pages 366-374, June.
    20. Zhao, Xiaobing & Zhou, Xian, 2012. "Modeling gap times between recurrent events by marginal rate function," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 370-383.

    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:22:y:2016:i:2:d:10.1007_s10985-015-9327-y. 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.