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A semiparametric random effects model for multivariate competing risks data

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  • Thomas H. Scheike
  • Yanqing Sun
  • Mei-Jie Zhang
  • Tina Kold Jensen

Abstract

We propose a semiparametric random effects model for multivariate competing risks data when the failures of a particular type are of interest. Under this model, the marginal cumulative incidence functions follow a generalized semiparametric additive model. The associations between the cause-specific failure times can be studied through dependence parameters of copula functions that are allowed to depend on cluster-level covariates. A cross-odds ratio-type measure is proposed to describe the associations between cause-specific failure times, and its relationship to the dependence parameters is explored. We develop a two-stage estimation procedure where the marginal models are estimated in the first stage and the dependence parameters are estimated in the second stage. The large sample properties of the proposed estimators are derived. The proposed procedures are applied to Danish twin data to model the cumulative incidence for the age of natural menopause and to investigate the association in the onset of natural menopause between monozygotic and dizygotic twins. Copyright 2010, Oxford University Press.

Suggested Citation

  • Thomas H. Scheike & Yanqing Sun & Mei-Jie Zhang & Tina Kold Jensen, 2010. "A semiparametric random effects model for multivariate competing risks data," Biometrika, Biometrika Trust, vol. 97(1), pages 133-145.
    • Handle: RePEc:oup:biomet:v:97:y:2010:i:1:p:133-145
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    File URL: http://hdl.handle.net/10.1093/biomet/asp082
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    1. repec:jss:jstsof:38:i02 is not listed on IDEAS
    2. 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.
    3. 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.
    4. Bingqing Zhou & Aurelien Latouche & Vanderson Rocha & Jason Fine, 2011. "Competing Risks Regression for Stratified Data," Biometrics, The International Biometric Society, vol. 67(2), pages 661-670, June.

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