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Modelling multivariate failure time associations in the presence of a competing risk

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  • Karen Bandeen-Roche

Abstract

There has been much research on analysing multivariate failure times, but little that has accommodated failures that arise in the presence of a competing failure process. This paper studies the problem of describing associations among times to such failures. It proposes a modified conditional hazard ratio measure of association that is tailored to competing risks data, develops frailty models and a nonparametric method for describing the proposed measure, and contrasts estimation by proposed methods with the 'standard' of treating competing risks as independently censoring failure times due to targeted causes. The methods are investigated on simulated and real data. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:2:p:299-314
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    Citations

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    Cited by:

    1. Richard Arnold & Stefanka Chukova & Yu Hayakawa, 2016. "Failure distributions in multicomponent systems with imperfect repairs," Journal of Risk and Reliability, , vol. 230(1), pages 4-17, February.
    2. 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.
    3. Joanna H. Shih & Paul S. Albert, 2010. "Modeling Familial Association of Ages at Onset of Disease in the Presence of Competing Risk," Biometrics, The International Biometric Society, vol. 66(4), pages 1012-1023, December.
    4. Dongdong Li & X. Joan Hu & Mary L. McBride & John J. Spinelli, 2020. "Multiple event times in the presence of informative censoring: modeling and analysis by copulas," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 573-602, July.
    5. 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.
    6. Jing Ning & Karen Bandeen-Roche, 2014. "Estimation of time-dependent association for bivariate failure times in the presence of a competing risk," Biometrics, The International Biometric Society, vol. 70(1), pages 10-20, March.
    7. Yu Cheng & Jason P. Fine & Michael R. Kosorok, 2009. "Nonparametric Association Analysis of Exchangeable Clustered Competing Risks Data," Biometrics, The International Biometric Society, vol. 65(2), pages 385-393, June.
    8. 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.
    9. 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.
    10. 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.

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