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The proportional odds cumulative incidence model for competing risks

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  • Frank Eriksson
  • Jianing Li
  • Thomas Scheike
  • Mei‐Jie Zhang

Abstract

We suggest an estimator for the proportional odds cumulative incidence model for competing risks data. The key advantage of this model is that the regression parameters have the simple and useful odds ratio interpretation. The model has been considered by many authors, but it is rarely used in practice due to the lack of reliable estimation procedures. We suggest such procedures and show that their performance improve considerably on existing methods. We also suggest a goodness‐of‐fit test for the proportional odds assumption. We derive the large sample properties and provide estimators of the asymptotic variance. The method is illustrated by an application in a bone marrow transplant study and the finite‐sample properties are assessed by simulations.

Suggested Citation

  • Frank Eriksson & Jianing Li & Thomas Scheike & Mei‐Jie Zhang, 2015. "The proportional odds cumulative incidence model for competing risks," Biometrics, The International Biometric Society, vol. 71(3), pages 687-695, September.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:3:p:687-695
    DOI: 10.1111/biom.12330
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    References listed on IDEAS

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    1. Malka Gorfine & David M. Zucker & Li Hsu, 2006. "Prospective survival analysis with a general semiparametric shared frailty model: A pseudo full likelihood approach," Biometrika, Biometrika Trust, vol. 93(3), pages 735-741, September.
    2. Thomas H. Scheike & Mei‐Jie Zhang, 2007. "Direct Modelling of Regression Effects for Transition Probabilities in Multistate Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 17-32, March.
    3. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
    4. Per Kragh Andersen, 2003. "Generalised linear models for correlated pseudo-observations, with applications to multi-state models," Biometrika, Biometrika Trust, vol. 90(1), pages 15-27, March.
    5. Torben Martinussen, 2002. "A flexible additive multiplicative hazard model," Biometrika, Biometrika Trust, vol. 89(2), pages 283-298, June.
    6. J. P. Fine, 1999. "Analysing competing risks data with transformation models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 817-830.
    7. Torben Martinussen & Thomas H. Scheike & David M. Zucker, 2011. "The Aalen additive gamma frailty hazards model," Biometrika, Biometrika Trust, vol. 98(4), pages 831-843.
    8. Kani Chen, 2002. "Semiparametric analysis of transformation models with censored data," Biometrika, Biometrika Trust, vol. 89(3), pages 659-668, August.
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