We propose a new class of semiparametric frailty models for spatially correlated survival
data. Specifically, we extend the ordinary frailty models by allowing random effects …

The use of survival models involving a random effect or 'frailty'term is becoming more
common. Usually the random effects are assumed to represent different clusters, and …

In survival analysis, frailty models are potential choices for modeling unexplained
heterogeneity in a population. This tutorial presents an overview and general framework of …

The shared frailty models allow for unobserved heterogeneity or for statistical dependence
between observed survival data. The most commonly used estimation procedure in frailty …

Frailty models are effective in broadening the class of survival models and inducing
dependence in multivariate survival distributions. In proportional hazards, the random effect …

We present the frailtyHL package for fitting semi-parametric frailty models using hlikelihood.
This package allows lognormal or gamma frailties for random-effect distribution, and it fits …

The frailty model is a random effect survival model, which allows for unobserved
heterogeneity or for statistical dependence between observed survival data. The nested …

The assessment of the effects of covariates or potential risk factors on the times to events in
a complex longitudinal study is an important problem in survival analysis. In such studies …

Many biological and medical studies have as a response of interest the time to occurrence of
some event, such as the occurrence of a particular symptom or disease, remission, relapse …

The hazard function plays a central role in survival analysis. In a homogeneous population,
the distribution of the time to event, described by the hazard, is the same for each individual …