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Prospective survival analysis with a general semiparametric shared frailty model: A pseudo full likelihood approach

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  • Malka Gorfine
  • David M. Zucker
  • Li Hsu

Abstract

We provide a simple estimation procedure for a general frailty model for the analysis of prospective correlated failure times. The large-sample properties of the proposed estimators of both the regression coefficient vector and the dependence parameter are described, and consistent variance estimators are given. A brief outline of the proofs is given. In a simulation study under the widely used gamma frailty model, our proposed approach was found to have essentially the same efficiency as the EM-based maximum likelihood approach considered by other authors, with negligible difference between the standard errors of the two estimators. However, the proposed approach provides a framework capable of handling general frailty distributions with finite moments and yields an explicit consistent variance estimator. Copyright 2006, Oxford University Press.

Suggested Citation

  • 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.
  • Handle: RePEc:oup:biomet:v:93:y:2006:i:3:p:735-741
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    File URL: http://hdl.handle.net/10.1093/biomet/93.3.735
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    Cited by:

    1. Frank Eriksson & Jianing Li & Thomas Scheike & Meiā€Jie Zhang, 2015. "The proportional odds cumulative incidence model for competing risks," Biometrics, The International Biometric Society, vol. 71(3), pages 687-695, September.
    2. L. Xue & L. Wang & A. Qu, 2010. "Incorporating Correlation for Multivariate Failure Time Data When Cluster Size Is Large," Biometrics, The International Biometric Society, vol. 66(2), pages 393-404, June.
    3. 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.
    4. Paulsen, Jostein & Lunde, Astrid & Skaug, Hans Julius, 2008. "Fitting mixed-effects models when data are left truncated," Insurance: Mathematics and Economics, Elsevier, vol. 43(1), pages 121-133, August.

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