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ABSTRACT: Recently, with the growth of statistical developments for competing risks analysis, some methods have been proposed to compute sample size in this context. These methods differ from a modelling approach: one is based on the Cox regression model for the cause-specific hazard, while another relies on the Fine and Gray regression model for the subdistribution hazard of a competing risk. In this work, we compare these approaches, derive a new sample size for comparing cumulative incidence functions when the hazards are not proportional (either cause-specific or subdistribution) and give practical advices to choose the approach best suited for the study question.
Statistics in Medicine 01/2008; 26(30):5370-80. · 1.88 Impact Factor
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Statistics in Medicine 09/2007; 26(19):3676-9; author reply 3679-80. · 1.88 Impact Factor
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ABSTRACT: We consider a competing risks setting, when evaluating the prognostic influence of an exposure on a specific cause of failure. Two main regression models are used in such analyses, the Cox cause-specific proportional hazards model and the subdistribution proportional hazards model. They are exemplified in a real data example focusing on relapse-free interval in acute leukaemia patients. We examine the properties of the estimator based on the latter model when the true model is the former. An explicit relationship between subdistribution hazards ratio and cause-specific hazards ratio is derived, assuming a flexible parametric distribution for latent failure times.
Statistics in Medicine 03/2007; 26(5):965-74. · 1.88 Impact Factor
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Statistics in Medicine 02/2007; 26(19):3676 - 3679. · 1.88 Impact Factor
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ABSTRACT: Recently, regression analysis of the cumulative incidence function has gained interest in competing risks data analysis, through the model proposed by Fine and Gray (JASA 1999; 94: 496-509). In this note, we point out that inclusion of time-dependent covariates in this model can lead to serious bias. We illustrate the problems arising in such a context, using bone marrow transplant data as a working example and numerical simulations. Practical advices are given, preventing the misuse of this model.
Biometrical Journal 01/2006; 47(6):807-14. · 1.25 Impact Factor
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ABSTRACT: Recently, regression analysis of the cumulative incidence function has gained interest in competing risks data analysis, through the model proposed by Fine and Gray (JASA 1999; 94: 496–509). In this note, we point out that inclusion of time-dependent covariates in this model can lead to serious bias. We illustrate the problems arising in such a context, using bone marrow transplant data as a working example and numerical simulations. Practical advices are given, preventing the misuse of this model. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Biometrical Journal 11/2005; 47(6):807 - 814. · 1.25 Impact Factor
