Correlated time-to-event data are commonly encounted in biomedical research, often as recurrent e v ent data or clustered data. To accommodate the correlation among survival times within the proportional hazards model framework, frailty m o d e l s h a ve beendeveloped. Clustered and recurrent event data may require two frailties, leading to a need for appropriate tests for frailty model select...

background : time to donating blood plays a major role in a regular donor to becoming continues one. the aim of this study was to determine the effective factors on the interval between the blood donations. methods : in a longitudinal study in 2008, 864 samples of first-time donors in shahrekord blood transfusion center,  capital city of chaharmahal and bakhtiari province, iran were selected by...

Many biomedical studies collect data on times of occurrence for a health event that can occur repeatedly, such as infection, hospitalization, recurrence of disease, or tumor onset. To analyze such data, it is necessary to account for within-subject dependency in the multiple event times. Motivated by data from studies of palpable tumors, this article proposes a dynamic frailty model and Bayesia...

Abstract. We present a Bayesian analysis for recurrent events data using a nonhomogeneous mixed Poisson point process with a dynamic subject-specific frailty function and a dynamic baseline intensity func- tion. The dynamic subject-specific frailty employs a dynamic piecewise constant function with a known pre-specified grid and the baseline in- tensity uses an unknown grid for the piecewise ...

Evolving definitions of frailty, and improved understanding of molecular and physiological declines in multiple systems that may increase vulnerability in frail, older adults has encouraged investigators from many disciplines to contribute to this emerging field of research. This article reports on the results of the 2004 American Geriatrics Society/National Institute on Aging conference on a R...

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...