We suggest a cure-mixture model to analyze bivariate time-to-event data, as motivated by the paper of Chatterjee and Shih (2001, Biometrics 57, 779 786), but with a simpler estimation procedure and the correlated gamma-frailty model instead of the shared gamma-frailty model. This approach allows us to deal with left truncated and right censored lifetime data and accounts for heterogeneity as we...

INTRODUCTION Oesophageal cancer is one of the most common causes of cancer mortality in developing countries, including Iran. This study aimed to assess factors affecting survival of patients with oesophageal cancer using parametric analysis with frailty models. METHODS Data on 359 patients with oesophageal cancer was collected from the Babol Cancer Registry for the period 1990-1991. By 2006,...

The ethical, logistical, and biological complications of working with an older population of people inherently limits clinical studies of frailty. The recent development of animal models of frailty, and tools for assessing frailty in animal models provides an invaluable opportunity for frailty research. This review summarizes currently published animal models of frailty including the interleuki...

In survival analysis, frailty models are potential choices for modeling unexplained heterogeneity in a population. This tutorial presents an overview and general framework of frailty modeling and estimation for multiplicative hazards models in the context of biomedical and genetic studies. Other topics in frailty models, such as diagnostic methods for model adequacy and inference in frailty mod...

Introducing a random effect into the Cox model is a useful tool forbuilding hierarchical families of univariate semiparametric regressionsurvival models. Hougaard [1984] used the Laplace transform to buildfrailty models with explicitly defined survival functions and random ef-fects. The family derived from stable distributions was then extended[Aalen, 1992] to frailty va...

A common way of allowing heterogeneity between individuals in models for lifetime data is to introduce an unobservable individual random effect Z. In a proportional hazards framework, the individual’s hazard becomes zhb(t) where hb(t) is the baseline hazard. The random variable Z is often assumed to follow the Gamma or Inverse Gaussian distribution. We develop here diagnostic tests for these as...