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Shared frailty model for recurrent event data with multiple causes

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  • P. G. Sankaran
  • P. Anisha

Abstract

The topic of heterogeneity in the analysis of recurrent event data has received considerable attention recent times. Frailty models are widely employed in such situations as they allow us to model the heterogeneity through common random effect. In this paper, we introduce a shared frailty model for gap time distributions of recurrent events with multiple causes. The parameters of the model are estimated using EM algorithm. An extensive simulation study is used to assess the performance of the method. Finally, we apply the proposed model to a real-life data.

Suggested Citation

  • P. G. Sankaran & P. Anisha, 2011. "Shared frailty model for recurrent event data with multiple causes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2859-2868, February.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2859-2868
    DOI: 10.1080/02664763.2011.570321
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    References listed on IDEAS

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    Cited by:

    1. P. G. Sankaran & S. Hari, 2024. "Semiparametric regression analysis of window-observation recurrent event data with multiple causes of failure," METRON, Springer;Sapienza Università di Roma, vol. 82(2), pages 201-212, August.
    2. M. S. Sisuma & P. G. Sankaran, 2022. "Non-parametric test of recurrent cumulative incidence functions for competing risks models," METRON, Springer;Sapienza Università di Roma, vol. 80(3), pages 331-342, December.
    3. Sankaran, P.G. & Anisha, P., 2012. "Additive hazards models for gap time data with multiple causes," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1454-1462.
    4. Lo, Simon M.S. & Mammen, Enno & Wilke, Ralf A., 2020. "A nested copula duration model for competing risks with multiple spells," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).

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