Frailty models are generally used to model heterogeneity between the individuals. The
distribution of the frailty variable is often assumed to be continuous. However, there are …

Frailty models are generally used to model heterogeneity and dependence between
individuals. The distribution of the frailty variable is often assumed to be continuous …

The semiparametric Cox regression model is often fitted in the modeling of survival data.
One of its main advantages is the ease of interpretation, as long as the hazards rates for two …

Frailty and resilience models provide a convenient tool to introduce random effects,
dependence and unobserved heterogeneity into the models for survival data. In this paper …

Frailty models are used for modeling heterogeneity in the data analysis of lifetimes. Analysis
that ignore frailty when it is present leads to incorrect inferences. In survival analysis, the …

Survival models with a frailty term are presented as an extension of Cox's proportional
hazard model, in which a random effect is introduced in the hazard function in a …

In this paper, we propose extending proportional hazards frailty models to allow a discrete
distribution for the frailty variable. Having zero frailty can be interpreted as being immune or …

Often in medical studies, there are settings in which it is meaningful to consider the
existence of a fraction of individuals who have little to no risk of experiencing the event of …

The concept of frailty offers a convenient way to introduce unobserved heterogeneity and
associations into models for survival data. In its simplest form, frailty is an unobserved …

The classical multiplicative frailty model in survival analysis accounts for unobserved
heterogeneity between individuals. It is of great importance to identify how the variation of …