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Pairwise dependence diagnostics for clustered failure-time data

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  • David V. Glidden

Abstract

Frailty and copula models specify a parametric dependence structure for multivariate failure-time data. Estimation of some joint quantities can be highly sensitive to the assumed parametric form, and hence model fit is an important issue. This paper lays out a general diagnostic framework for evaluating and selecting frailty and copula models. The approach is based on the cumulative sum of residuals that are calculated in bivariate time. The residuals reflect the difference between the observed and expected bivariate association structures. The proposed model-checking process is interpretable with a limiting distribution which can be approximated using the bootstrap. Simulations and a data example illustrate the practical application of the method. Copyright 2007, Oxford University Press.

Suggested Citation

  • David V. Glidden, 2007. "Pairwise dependence diagnostics for clustered failure-time data," Biometrika, Biometrika Trust, vol. 94(2), pages 371-385.
    • Handle: RePEc:oup:biomet:v:94:y:2007:i:2:p:371-385
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    File URL: http://hdl.handle.net/10.1093/biomet/asm024
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    Cited by:

    1. Steven Abrams & Paul Janssen & Jan Swanepoel & Noël Veraverbeke, 2020. "Nonparametric estimation of the cross ratio function," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 771-801, June.
    2. Guoqing Diao & Guosheng Yin, 2012. "A general transformation class of semiparametric cure rate frailty models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(5), pages 959-989, October.

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