IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v26y2020i1d10.1007_s10985-019-09464-2.html
   My bibliography  Save this article

Frailty modelling approaches for semi-competing risks data

Author

Listed:
  • Il Do Ha

    (Pukyong National University)

  • Liming Xiang

    (Nanyang Technological University)

  • Mengjiao Peng

    (Nanyang Technological University)

  • Jong-Hyeon Jeong

    (University of Pittsburgh)

  • Youngjo Lee

    (Seoul National University)

Abstract

In the semi-competing risks situation where only a terminal event censors a non-terminal event, observed event times can be correlated. Recently, frailty models with an arbitrary baseline hazard have been studied for the analysis of such semi-competing risks data. However, their maximum likelihood estimator can be substantially biased in the finite samples. In this paper, we propose effective modifications to reduce such bias using the hierarchical likelihood. We also investigate the relationship between marginal and hierarchical likelihood approaches. Simulation results are provided to validate performance of the proposed method. The proposed method is illustrated through analysis of semi-competing risks data from a breast cancer study.

Suggested Citation

  • Il Do Ha & Liming Xiang & Mengjiao Peng & Jong-Hyeon Jeong & Youngjo Lee, 2020. "Frailty modelling approaches for semi-competing risks data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 109-133, January.
    • Handle: RePEc:spr:lifeda:v:26:y:2020:i:1:d:10.1007_s10985-019-09464-2
    DOI: 10.1007/s10985-019-09464-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-019-09464-2
    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/s10985-019-09464-2?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. Samuli Ripatti & Juni Palmgren, 2000. "Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood," Biometrics, The International Biometric Society, vol. 56(4), pages 1016-1022, December.
    2. Georg Heinze & Michael Schemper, 2001. "A Solution to the Problem of Monotone Likelihood in Cox Regression," Biometrics, The International Biometric Society, vol. 57(1), pages 114-119, March.
    3. Noh, Maengseok & Lee, Youngjo, 2007. "REML estimation for binary data in GLMMs," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 896-915, May.
    4. Kyu Ha Lee & Sebastien Haneuse & Deborah Schrag & Francesca Dominici, 2015. "Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(2), pages 253-273, February.
    5. Il Do Ha & Maengseok Noh & Youngjo Lee, 2010. "Bias Reduction of Likelihood Estimators in Semiparametric Frailty Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 307-320, June.
    6. Ha, Il Do & Sylvester, Richard & Legrand, Catherine & MacKenzie, Gilbert, 2011. "Frailty modelling for survival data from multi-centre clinical trials," LIDAM Reprints ISBA 2011060, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Jinfeng Xu & John D. Kalbfleisch & Beechoo Tai, 2010. "Statistical Analysis of Illness–Death Processes and Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 66(3), pages 716-725, September.
    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. Annalisa Orenti & Patrizia Boracchi & Giuseppe Marano & Elia Biganzoli & Federico Ambrogi, 2022. "A pseudo-values regression model for non-fatal event free survival in the presence of semi-competing risks," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 709-727, September.

    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. Fei Jiang & Sebastien Haneuse, 2017. "A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 112-129, March.
    2. Harrison T. Reeder & Junwei Lu & Sebastien Haneuse, 2023. "Penalized estimation of frailty‐based illness–death models for semi‐competing risks," Biometrics, The International Biometric Society, vol. 79(3), pages 1657-1669, September.
    3. Yang Li & Hao Liu & Xiaoshen Wang & Wanzhu Tu, 2022. "Semi‐parametric time‐to‐event modelling of lengths of hospital stays," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1623-1647, November.
    4. Lea Kats & Malka Gorfine, 2023. "An accelerated failure time regression model for illness–death data: A frailty approach," Biometrics, The International Biometric Society, vol. 79(4), pages 3066-3081, December.
    5. Kyu Ha Lee & Virginie Rondeau & Sebastien Haneuse, 2017. "Accelerated failure time models for semi‐competing risks data in the presence of complex censoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1401-1412, December.
    6. Derek Dinart & Carine Bellera & Virginie Rondeau, 2022. "Sample size estimation for cancer randomized trials in the presence of heterogeneous populations," Biometrics, The International Biometric Society, vol. 78(4), pages 1662-1673, December.
    7. Peng, Mengjiao & Xiang, Liming & Wang, Shanshan, 2018. "Semiparametric regression analysis of clustered survival data with semi-competing risks," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 53-70.
    8. Daniel Nevo & Deborah Blacker & Eric B. Larson & Sebastien Haneuse, 2022. "Modeling semi‐competing risks data as a longitudinal bivariate process," Biometrics, The International Biometric Society, vol. 78(3), pages 922-936, September.
    9. Yen‐Tsung Huang, 2021. "Causal mediation of semicompeting risks," Biometrics, The International Biometric Society, vol. 77(4), pages 1143-1154, December.
    10. Xifen Huang & Jinfeng Xu & Hao Guo & Jianhua Shi & Wenjie Zhao, 2022. "An MM Algorithm for the Frailty-Based Illness Death Model with Semi-Competing Risks Data," Mathematics, MDPI, vol. 10(19), pages 1-13, October.
    11. Yen‐Tsung Huang, 2021. "Rejoinder to “Causal mediation of semicompeting risks”," Biometrics, The International Biometric Society, vol. 77(4), pages 1170-1174, December.
    12. Qui Tran & Kelley M. Kidwell & Alex Tsodikov, 2018. "A joint model of cancer incidence, metastasis, and mortality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 385-406, July.
    13. Chau, Nancy H. & Qin, Yu & Zhang, Weiwen, 2015. "Networked Leaders in the Shadow of the Market – A Chinese Experiment in Allocating Land Conversion Rights," Working Papers 250022, Cornell University, Department of Applied Economics and Management.
    14. Il Do Ha & Maengseok Noh & Youngjo Lee, 2010. "Bias Reduction of Likelihood Estimators in Semiparametric Frailty Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 307-320, June.
    15. Annalisa Orenti & Patrizia Boracchi & Giuseppe Marano & Elia Biganzoli & Federico Ambrogi, 2022. "A pseudo-values regression model for non-fatal event free survival in the presence of semi-competing risks," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 709-727, September.
    16. Guillaume Horny & Dragana Djurdjevic & Bernhard Boockmann & François Laisney, 2008. "Bayesian Estimation of Cox Models with Non-nested Random Effects: an Application to the Ratification Of ILO Conventions by Developing Countries," Annals of Economics and Statistics, GENES, issue 89, pages 193-214.
    17. Goele Massonnet & Paul Janssen & Tomasz Burzykowski, 2008. "Fitting Conditional Survival Models to Meta‐Analytic Data by Using a Transformation Toward Mixed‐Effects Models," Biometrics, The International Biometric Society, vol. 64(3), pages 834-842, September.
    18. Pianto, Donald M. & Cribari-Neto, Francisco, 2011. "Dealing with monotone likelihood in a model for speckled data," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1394-1409, March.
    19. Lior Rennert & Zichen Ma & Christopher S. McMahan & Delphine Dean, 2022. "Effectiveness and protection duration of Covid-19 vaccines and previous infection against any SARS-CoV-2 infection in young adults," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    20. Menggang Yu & Constantin T. Yiannoutsos, 2015. "Marginal and Conditional Distribution Estimation from Double-sampled Semi-competing Risks Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 87-103, March.

    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:26:y:2020:i:1:d:10.1007_s10985-019-09464-2. 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.