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Additive hazards models for gap time data with multiple causes

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  • Sankaran, P.G.
  • Anisha, P.

Abstract

Recurrent event data with multiple causes are often observed in biomedical studies. The additive hazards model describes a different aspect of the association between covariates and the failure time than does the proportional hazards model. In this paper, we introduce additive hazards models for the analysis of gap time data of recurrent events with multiple causes. We estimate the regression parameter vector and cumulative baseline cause specific hazard rate function using counting process approach. Asymptotic properties of the estimators are studied. The proposed model is applied to the kidney dialysis data given in Lawless (2003). A simulation study is carried out to assess the performance of the estimates.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:7:p:1454-1462
    DOI: 10.1016/j.spl.2012.02.025
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    References listed on IDEAS

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    1. J. F. Lawless & M. B. Wigg & S. Tuli & J. Drake & M. Lamberti‐Pasculli, 2001. "Analysis of repeated failures or durations, with application to shunt failures for patients with paediatric hydrocephalus," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 449-465.
    2. Robert L. Strawderman, 2005. "The accelerated gap times model," Biometrika, Biometrika Trust, vol. 92(3), pages 647-666, September.
    3. Wang M-C. & Qin J. & Chiang C-T., 2001. "Analyzing Recurrent Event Data With Informative Censoring," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1057-1065, September.
    4. Douglas E. Schaubel, 2004. "Regression methods for gap time hazard functions of sequentially ordered multivariate failure time data," Biometrika, Biometrika Trust, vol. 91(2), pages 291-303, June.
    5. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    6. 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.
    7. Martin G. Larson & Gregg E. Dinse, 1985. "A Mixture Model for the Regression Analysis of Competing Risks Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(3), pages 201-211, November.
    8. V. N. Sreeja & P. G. Sankaran, 2007. "Proportional mean residual life model for gap time distributions of recurrent events," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 319-336.
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    Cited by:

    1. 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.

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