IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v67y2011i2p415-426.html
   My bibliography  Save this article

Frailty-Based Competing Risks Model for Multivariate Survival Data

Author

Listed:
  • Malka Gorfine
  • Li Hsu

Abstract

No abstract is available for this item.

Suggested Citation

  • Malka Gorfine & Li Hsu, 2011. "Frailty-Based Competing Risks Model for Multivariate Survival Data," Biometrics, The International Biometric Society, vol. 67(2), pages 415-426, June.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:2:p:415-426
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2010.01470.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Karen Bandeen-Roche, 2002. "Modelling multivariate failure time associations in the presence of a competing risk," Biometrika, Biometrika Trust, vol. 89(2), pages 299-314, June.
    2. Bingshu E. Chen & Joan L. Kramer & Mark H. Greene & Philip S. Rosenberg, 2008. "Competing Risks Analysis of Correlated Failure Time Data," Biometrics, The International Biometric Society, vol. 64(1), pages 172-179, March.
    3. D. Zeng & D. Y. Lin, 2007. "Maximum likelihood estimation in semiparametric regression models with censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 507-564, September.
    4. J. Fan & R. L. Prentice & L. Hsu, 2000. "A class of weighted dependence measures for bivariate failure time data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 181-190.
    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. Frank Eriksson & Thomas Scheike, 2015. "Additive gamma frailty models with applications to competing risks in related individuals," Biometrics, The International Biometric Society, vol. 71(3), pages 677-686, September.
    2. Yujie Zhong & Richard J. Cook, 2018. "Second-Order Estimating Equations for Clustered Current Status Data from Family Studies Using Response-Dependent Sampling," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 160-183, April.
    3. Sai H. Dharmarajan & Douglas E. Schaubel & Rajiv Saran, 2018. "Evaluating center performance in the competing risks setting: Application to outcomes of wait†listed end†stage renal disease patients," Biometrics, The International Biometric Society, vol. 74(1), pages 289-299, March.
    4. Holst, Klaus K. & Scheike, Thomas H. & Hjelmborg, Jacob B., 2016. "The liability threshold model for censored twin data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 324-335.
    5. Jeongyong Kim & Karen Bandeen-Roche, 2019. "Parametric estimation of association in bivariate failure-time data subject to competing risks: sensitivity to underlying assumptions," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 259-279, April.
    6. 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.
    7. Emanuel Krebs & Jeong E. Min & Elizabeth Evans & Libo Li & Lei Liu & David Huang & Darren Urada & Thomas Kerr & Yih-Ing Hser & Bohdan Nosyk, 2017. "Estimating State Transitions for Opioid Use Disorders," Medical Decision Making, , vol. 37(5), pages 483-497, July.
    8. Wang Hao & Cheng Yu, 2014. "Piecewise Cause-Specific Association Analyses of Multivariate Untied or Tied Competing Risks Data," The International Journal of Biostatistics, De Gruyter, vol. 10(2), pages 197-220, November.
    9. Juhee Lee & Peter F. Thall & Pavlos Msaouel, 2023. "Bayesian treatment screening and selection using subgroup‐specific utilities of response and toxicity," Biometrics, The International Biometric Society, vol. 79(3), pages 2458-2473, September.
    10. 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.
    11. Alexander Begun & Anatoli Yashin, 2019. "Study of the bivariate survival data using frailty models based on Lévy processes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 37-67, March.
    12. Ungolo, Francesco & van den Heuvel, Edwin R., 2024. "A Dirichlet process mixture regression model for the analysis of competing risk events," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 95-113.
    13. Ying Zhou & Liang Wang & Tzong-Ru Tsai & Yogesh Mani Tripathi, 2023. "Estimation of Dependent Competing Risks Model with Baseline Proportional Hazards Models under Minimum Ranked Set Sampling," Mathematics, MDPI, vol. 11(6), pages 1-30, March.

    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. Frank Eriksson & Thomas Scheike, 2015. "Additive gamma frailty models with applications to competing risks in related individuals," Biometrics, The International Biometric Society, vol. 71(3), pages 677-686, September.
    2. Lu Chen & Li Hsu & Kathleen Malone, 2009. "A Frailty-Model-Based Approach to Estimating the Age-Dependent Penetrance Function of Candidate Genes Using Population-Based Case-Control Study Designs: An Application to Data on the BRCA1 Gene," Biometrics, The International Biometric Society, vol. 65(4), pages 1105-1114, December.
    3. Jin Wang & Donglin Zeng & D. Y. Lin, 2022. "Semiparametric single-index models for optimal treatment regimens with censored outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 744-763, October.
    4. Changrong Yan & Dixin Zhang, 2013. "Sparse dimension reduction for survival data," Computational Statistics, Springer, vol. 28(4), pages 1835-1852, August.
    5. Mário Castro & Ming-Hui Chen & Joseph G. Ibrahim & John P. Klein, 2014. "Bayesian Transformation Models for Multivariate Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 187-199, March.
    6. 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.
    7. Yanqing Sun & Rajeshwari Sundaram & Yichuan Zhao, 2009. "Empirical Likelihood Inference for the Cox Model with Time‐dependent Coefficients via Local Partial Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 444-462, September.
    8. Chen, Songxi, 2012. "Estimation in semiparametric models with missing data," MPRA Paper 46216, University Library of Munich, Germany.
    9. Pao-sheng Shen, 2012. "Analysis of left-truncated right-censored or doubly censored data with linear transformation models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 584-603, September.
    10. Mingzhe Wu & Ming Zheng & Wen Yu & Ruofan Wu, 2018. "Estimation and variable selection for semiparametric transformation models under a more efficient cohort sampling design," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 570-596, September.
    11. 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.
    12. Chyong-Mei Chen & Pao-sheng Shen & Yi Liu, 2021. "On semiparametric transformation model with LTRC data: pseudo likelihood approach," Statistical Papers, Springer, vol. 62(1), pages 3-30, February.
    13. Gunky Kim & Mervyn J. Silvapulle & Paramsothy Silvapulle, 2007. "Semiparametric estimation of the dependence parameter of the error terms in multivariate regression," Monash Econometrics and Business Statistics Working Papers 1/07, Monash University, Department of Econometrics and Business Statistics.
    14. Juma Aseed Mohamed Buajela & Sadun Naser Yassin Alheety, 2020. "The Effect of Career Commitment on Productivity in The Construction Sector of Libya: A pilot study," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 4(12), pages 351-358, December.
    15. Antonio Lijoi & Bernardo Nipoti, 2013. "A class of hazard rate mixtures for combining survival data from different experiments," DEM Working Papers Series 059, University of Pavia, Department of Economics and Management.
    16. K. Burke & G. MacKenzie, 2017. "Multi-parameter regression survival modeling: An alternative to proportional hazards," Biometrics, The International Biometric Society, vol. 73(2), pages 678-686, June.
    17. Christoph Breunig & Peter Haan, 2018. "Nonparametric Regression with Selectively Missing Covariates," Papers 1810.00411, arXiv.org, revised Oct 2020.
    18. Mondal, Shoubhik & Subramanian, Sundarraman, 2014. "Model assisted Cox regression," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 281-303.
    19. Fengchang Lin & Jason P. Fine, 2009. "Pseudomartingale estimating equations for modulated renewal process models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 3-23, January.
    20. Lei Liu & Xuelin Huang, 2009. "Joint analysis of correlated repeated measures and recurrent events processes in the presence of death, with application to a study on acquired immune deficiency syndrome," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 65-81, February.

    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:bla:biomet:v:67:y:2011:i:2:p:415-426. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.