Title of article :
Inference for serological surveys investigating past exposures to infections resulting in long-lasting immunity – an approach using finite mixture models with concomitant information
Author/Authors :
Irina Chis Ster، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Abstract :
This paper is concerned with developing a latent class mixture modelling technique which efficiently
exploits data from serological surveys aiming to investigate past exposures to infections resulting in longterm
or life-lasting immunity.Mixture components featured by antibody assays’ distribution are associated
with the serological groups in the population, whilst the probability mixture that an individual belongs to
the positive serological group is regarded as an age-dependent prevalence. The latter embeds a mechanistic
model which explains the infection process, accounting for heterogeneities, contact patterns in the population
and incorporating elements of study design. A Bayesian framework for statistical inference using
Markov chain Monte Carlo estimation methods naturally accommodates missing responses in the data and
allows straightforward assessement of uncertainties in nonlinear models. The applicability of the method
is illustrated by investigating past exposure to varicella zoster virus infection in pre-school children, using
data from a large scale UK cohort study which included a cross-sectional serological survey based on oral
fluid samples.
Keywords :
finite mixtures with concomitant information , Muench’s catalyticmodel , age-dependent prevalence , MCMC , Bayesian inference , varicella zoster virus , Millennium Cohort Study , Oral fluid
Journal title :
JOURNAL OF APPLIED STATISTICS
Journal title :
JOURNAL OF APPLIED STATISTICS