IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v175y2022ics016794732200130x.html
   My bibliography  Save this article

Copula link-based additive models for bivariate time-to-event outcomes with general censoring scheme

Author

Listed:
  • Petti, Danilo
  • Eletti, Alessia
  • Marra, Giampiero
  • Radice, Rosalba

Abstract

Bivariate survival outcomes arise frequently in applied studies where the occurrence of two events of interest are associated. Often the exact event times are unknown due to censoring which can manifest in various forms. A general and flexible copula regression model that can handle bivariate survival data subject to various censoring mechanisms, which include a mixture of uncensored, left-, right-, and interval-censored data, is proposed. The proposal permits to specify all model parameters as flexible functions of covariate effects, flexibly model the baseline survival functions by means of monotonic P-splines, characterise the marginals via transformations of the survival functions which yield, e.g., the proportional hazards and odds models as special cases, and model the dependence between events using a wide variety of copulae. The algorithm is based on a computationally efficient and stable penalised maximum likelihood estimation approach with integrated automatic multiple smoothing parameter selection. The proposed model is evaluated in a simulation study and illustrated using data from the Age-Related Eye Disease Study. The modelling framework has been incorporated in the newly-revised R package GJRM, hence allowing any user to fit the desired model(s) and produce easy-to-interpret numerical and visual summaries.

Suggested Citation

  • Petti, Danilo & Eletti, Alessia & Marra, Giampiero & Radice, Rosalba, 2022. "Copula link-based additive models for bivariate time-to-event outcomes with general censoring scheme," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:csdana:v:175:y:2022:i:c:s016794732200130x
    DOI: 10.1016/j.csda.2022.107550
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016794732200130X
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2022.107550?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. Alessia Eletti & Giampiero Marra & Manuela Quaresma & Rosalba Radice & Francisco Javier Rubio, 2022. "A unifying framework for flexible excess hazard modelling with applications in cancer epidemiology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 1044-1062, August.
    2. Sujica, Aleksandar & Van Keilegom, Ingrid, 2018. "The copula-graphic estimator in censored nonparametric location-scale regression models," Econometrics and Statistics, Elsevier, vol. 7(C), pages 89-114.
    3. Lo, Simon M.S. & Mammen, Enno & Wilke, Ralf A., 2020. "A nested copula duration model for competing risks with multiple spells," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    4. Jose S. Romeo & Renate Meyer & Diego I. Gallardo, 2018. "Bayesian bivariate survival analysis using the power variance function copula," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 355-383, April.
    5. Qingning Zhou & Tao Hu & Jianguo Sun, 2017. "A Sieve Semiparametric Maximum Likelihood Approach for Regression Analysis of Bivariate Interval-Censored Failure Time Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 664-672, April.
    6. Wang, Naichen & Wang, Lianming & McMahan, Christopher S., 2015. "Regression analysis of bivariate current status data under the Gamma-frailty proportional hazards model using the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 140-150.
    7. Barthel, Nicole & Geerdens, Candida & Killiches, Matthias & Janssen, Paul & Czado, Claudia, 2018. "Vine copula based likelihood estimation of dependence patterns in multivariate event time data," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 109-127.
    8. Donglin Zeng & Fei Gao & D. Y. Lin, 2017. "Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data," Biometrika, Biometrika Trust, vol. 104(3), pages 505-525.
    9. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
    10. Vatter, Thibault & Chavez-Demoulin, Valérie, 2015. "Generalized additive models for conditional dependence structures," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 147-167.
    11. Giampiero Marra & Rosalba Radice, 2020. "Copula Link-Based Additive Models for Right-Censored Event Time Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 886-895, April.
    12. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
    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. Woraphon Yamaka & Paravee Maneejuk & Rungrapee Phadkantha & Wiranya Puntoon & Payap Tarkhamtham & Tatcha Sudtasan, 2023. "Survival and Duration Analysis of MSMEs in Chiang Mai, Thailand: Evidence from the Post-COVID-19 Recovery," Mathematics, MDPI, vol. 11(4), pages 1-21, 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. 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. Chun Yin Lee & Kin Yau Wong & K. F. Lam & Jinfeng Xu, 2022. "Analysis of clustered interval‐censored data using a class of semiparametric partly linear frailty transformation models," Biometrics, The International Biometric Society, vol. 78(1), pages 165-178, March.
    3. Marra, Giampiero & Radice, Rosalba, 2017. "Bivariate copula additive models for location, scale and shape," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 99-113.
    4. Kneib, Thomas & Silbersdorff, Alexander & Säfken, Benjamin, 2023. "Rage Against the Mean – A Review of Distributional Regression Approaches," Econometrics and Statistics, Elsevier, vol. 26(C), pages 99-123.
    5. Marra, Giampiero & Farcomeni, Alessio & Radice, Rosalba, 2021. "Link-based survival additive models under mixed censoring to assess risks of hospital-acquired infections," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    6. 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.
    7. Eleanderson Campos & Roel Braekers & Devanil J. Souza & Lucas M. Chaves, 2021. "Factor copula models for right-censored clustered survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(3), pages 499-535, July.
    8. Liuquan Sun & Shuwei Li & Lianming Wang & Xinyuan Song & Xuemei Sui, 2022. "Simultaneous variable selection in regression analysis of multivariate interval‐censored data," Biometrics, The International Biometric Society, vol. 78(4), pages 1402-1413, December.
    9. 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).
    10. Hazem Krichene & Abhijit Chakraborty & Hiroyasu Inoue & Yoshi Fujiwara, 2017. "Business cycles’ correlation and systemic risk of the Japanese supplier-customer network," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-22, October.
    11. Maziar Sahamkhadam, 2021. "Dynamic copula-based expectile portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 209-223, May.
    12. Emmanoulides, Christos & Fousekis, Panos, 2014. "Vertical Price Transmission in the US Pork Industry: Evidence from Copula Models," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 15(1), pages 1-12.
    13. Dayu Sun & Yuanyuan Guo & Yang Li & Jianguo Sun & Wanzhu Tu, 2024. "A flexible time-varying coefficient rate model for panel count data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(4), pages 721-741, October.
    14. Beatrice D. Simo-Kengne & Kofi A. Ababio & Jules Mba & Ur Koumba, 2018. "Behavioral portfolio selection and optimization: an application to international stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(3), pages 311-328, August.
    15. Wu Zening & He Chentao & Huiliang Wang & Qian Zhang, 2020. "Reservoir Inflow Synchronization Analysis for Four Reservoirs on a Mainstream and its Tributaries in Flood Season Based on a Multivariate Copula Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2753-2770, July.
    16. Nagler Thomas & Schellhase Christian & Czado Claudia, 2017. "Nonparametric estimation of simplified vine copula models: comparison of methods," Dependence Modeling, De Gruyter, vol. 5(1), pages 99-120, January.
    17. Brechmann, Eike & Czado, Claudia & Paterlini, Sandra, 2014. "Flexible dependence modeling of operational risk losses and its impact on total capital requirements," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 271-285.
    18. Li, Haihe & Wang, Pan & Huang, Xiaoyu & Zhang, Zheng & Zhou, Changcong & Yue, Zhufeng, 2021. "Vine copula-based parametric sensitivity analysis of failure probability-based importance measure in the presence of multidimensional dependencies," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    19. Thabani Ndlovu & Delson Chikobvu, 2024. "The GARCH-EVT-Copula Approach to Investigating Dependence and Quantifying Risk in a Portfolio of Bitcoin and the South African Rand," JRFM, MDPI, vol. 17(11), pages 1-16, November.
    20. Yichen Lou & Peijie Wang & Jianguo Sun, 2023. "A semi-parametric weighted likelihood approach for regression analysis of bivariate interval-censored outcomes from case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 628-653, July.

    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:175:y:2022:i:c:s016794732200130x. 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.