Copulas and frailty models are important tools to model bivariate survival data. Equivalence between Archimedean copula models and shared frailty models, e.g. between the Clayton-Oakes copula model and the shared gamma frailty model, has often been claimed in the literature. In this note we show that, in both the models, there is indeed a well-known equivalence between the copula functions; the modeling of the marginal survival functions, however, is quite different. The latter fact leads to different joint survival functions.bivariate survival data, Clayton-Oakes copula, positive stable frailty, shared gamma frailty model,
In some past works by the author and collaborators, the notion of ageing function of an exchangeable...
In multivariate survival analyses, understanding and quantifying the association between survival ti...
We point out how capacities (non-additive measures) can be used to provide explanations about the co...
Dependence in survival analysis is most frequently modeled by the frailty model or the copula model....
In this dissertation we solve the nonidentifiability problem of Archimedean copula models based on d...
For the analysis of clustered survival data, two different types of model that take the association ...
This book introduces readers to advanced statistical methods for analyzing survival data involving c...
This dissertation has three independent parts. The first part studies a variation of the competing r...
The analysis of multivariate time-to-event (TTE) data can become complicated due to the presence of ...
In some recent papers, the authors considered a function B that describes the level curves of an exc...
Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Biostatistics and ...
For a couple of lifetimes (X1,X2) with an exchangeable joint survival function , attention is focuse...
We provide probabilistic explanations of equivalences, between conditions of positive dependence and...
Bivariate, semi-competing risk data are survival endpoints where a terminal event can censor a non-...
The hazard function plays a central role in survival analysis. In a homogeneous population, the dist...
In some past works by the author and collaborators, the notion of ageing function of an exchangeable...
In multivariate survival analyses, understanding and quantifying the association between survival ti...
We point out how capacities (non-additive measures) can be used to provide explanations about the co...
Dependence in survival analysis is most frequently modeled by the frailty model or the copula model....
In this dissertation we solve the nonidentifiability problem of Archimedean copula models based on d...
For the analysis of clustered survival data, two different types of model that take the association ...
This book introduces readers to advanced statistical methods for analyzing survival data involving c...
This dissertation has three independent parts. The first part studies a variation of the competing r...
The analysis of multivariate time-to-event (TTE) data can become complicated due to the presence of ...
In some recent papers, the authors considered a function B that describes the level curves of an exc...
Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Biostatistics and ...
For a couple of lifetimes (X1,X2) with an exchangeable joint survival function , attention is focuse...
We provide probabilistic explanations of equivalences, between conditions of positive dependence and...
Bivariate, semi-competing risk data are survival endpoints where a terminal event can censor a non-...
The hazard function plays a central role in survival analysis. In a homogeneous population, the dist...
In some past works by the author and collaborators, the notion of ageing function of an exchangeable...
In multivariate survival analyses, understanding and quantifying the association between survival ti...
We point out how capacities (non-additive measures) can be used to provide explanations about the co...