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An estimating equation for parametric shared frailty models with marginal additive hazards

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  • Christian Bressen Pipper
  • Torben Martinussen

Abstract

Summary. Multivariate failure time data arise when data consist of clusters in which the failure times may be dependent. A popular approach to such data is the marginal proportional hazards model with estimation under the working independence assumption. In some contexts, however, it may be more reasonable to use the marginal additive hazards model. We derive asymptotic properties of the Lin and Ying estimators for the marginal additive hazards model for multivariate failure time data. Furthermore we suggest estimating equations for the regression parameters and association parameters in parametric shared frailty models with marginal additive hazards by using the Lin and Ying estimators. We give the large sample properties of the estimators arising from these estimating equations and investigate their small sample properties by Monte Carlo simulation. A real example is provided for illustration.

Suggested Citation

  • Christian Bressen Pipper & Torben Martinussen, 2004. "An estimating equation for parametric shared frailty models with marginal additive hazards," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 207-220, February.
    • Handle: RePEc:bla:jorssb:v:66:y:2004:i:1:p:207-220
    DOI: 10.1046/j.1369-7412.2003.05305.x
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    Cited by:

    1. P. Seetharaman & Siddhartha Chib & Andrew Ainslie & Peter Boatwright & Tat Chan & Sachin Gupta & Nitin Mehta & Vithala Rao & Andrei Strijnev, 2005. "Models of Multi-Category Choice Behavior," Marketing Letters, Springer, vol. 16(3), pages 239-254, December.
    2. C. B. Pipper & C. Ritz & T. H. Scheike, 2011. "Explained Variation in a Fully Specified Model for Data-Grouped Survival Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1361-1368, December.
    3. Hui Li & Xiaogang Duan & Guosheng Yin, 2016. "Generalized Method of Moments for Additive Hazards Model with Clustered Dental Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1124-1139, December.
    4. Ali Sharifi & Reza Hashemi, 2023. "Semiparametric Model for Recurrent Event Data Under Two Independent Competing Risks with Excess Zero and Informative Censoring," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 633-650, February.
    5. Frank Eriksson & Torben Martinussen & Thomas H. Scheike, 2015. "Clustered Survival Data with Left-truncation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1149-1166, December.

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