For multivariate failure time data, we propose a new class of shared gamma frailty models by imposing the Box-Cox transformation on the hazard function, and the product of the baseline hazard and the frailty. This novel class of models allows for a very broad range of shapes and relationships between the hazard and baseline hazard functions. It includes the well-known Cox gamma frailty model an...

When analyzing correlated time to event data, shared frailty (random effect) models are particularly attractive. However, the estimation of such models has proved challenging. In semiparametric models, this is further complicated by the presence of the nonparametric baseline hazard. Although recent years have seen an increased availability of software for fitting frailty models, most software p...

In recent years, it has been shown that individual heterogeneity in the acquisition of infectious diseases has a large impact on the estimation of important epidemiological parameters such as the (basic) reproduction number. Therefore, frailty modeling has become increasingly popular in infectious disease epidemiology. However, so far, using frailty models, it was assumed infections confer life...

Recurrent events data have been increasingly important in clinical studies. There are many methods to analyze this type of data. Several papers have been presented about how to perform repeated events analysis in SAS. In clinical studies, we may encounter recurrent disease episodes in patients with a terminal event such as death, and the terminal event is often strongly correlated with the recu...

The joineR package implements methods for analysing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-toevent outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure. The model for the time-to-event outcome is a C...

Hidden differences in the survival chances of individuals in a population influence the shape of the mortality rate observed in demographic and epidemiological studies. To evaluate the contribution of such hidden variations to observed hazards, frailty models have been suggested. The application of these models to the analysis of survival data in demography, epidemiology, and biostatistics has ...

The importance of some recognized risk factors on genetic influences for coronary heart disease (CHD) needs further clarification. The aim of the present study was therefore to study the impact of known risk factors on genetic influences for CHD-death. Both twin (correlated gamma-frailty) and non-twin models (univariate gamma-frailty) were utilized and compared regarding their suitability for g...

abstract background: identify talented people to donate predisposed a major role in supplying healthy blood. if the time interval between donations decrease, the chance that voluntary of blood donation be a regular donor increase. the aim of this study was to determine factors affecting the interval between blood donations in a sample of first-time blood donors. methods: data of a cross-section...

Frailty models are used in survival analysis to account for unobserved heterogeneity in individual risks to disease and death. To analyze bivariate data on related survival times (e.g. matched pairs experiments, twin or family data), bivariate correlated frailty models were suggested, where the frailties were constructed using independent additive components. The purpose of this paper is to des...

This talk gives an overview about the analysis of twin data on diseases, aging and longevity. One goal of our research in this area is the evaluation of the genetic contribution to life span and mortality from different causes. Such evaluation is performed using the idea of correlated frailty. We apply different correlated frailty models to the analysis of bivariate survival data from Danish, F...