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Application of Markov chain Monte Carlo methods to modelling birth prevalence of Down syndrome

Bray, Issy; Wright, D. E.

Authors

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Issy Bray Issy.Bray@uwe.ac.uk
Associate Professor in Public Health (Epidemiology)

D. E. Wright



Abstract

Data collected before the routine application of prenatal screening are of unique value in estimating the natural live-birth prevalence of Down syndrome. However, much of these data are from births from over 20 years ago and they are of uncertain quality. In particular, they are subject to varying degrees of underascertainment. Published approaches have used ad hoc corrections to deal with this problem or have been restricted to data sets in which ascertainment is assumed to be complete. In this paper we adopt a Bayesian approach to modelling ascertainment and live-birth prevalence. We consider three prior specifications concerning ascertainment and compare predicted maternal-age-specific prevalence under these three different prior specifications. The computations are carried out by using Markov chain Monte Carlo methods in which model parameters and missing data are sampled.

Journal Article Type Article
Publication Date Jan 1, 2002
Journal Journal of the Royal Statistical Society: Series C (Applied Statistics)
Print ISSN 0035-9254
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 47
Issue 4
Pages 589-602
DOI https://doi.org/10.1111/1467-9876.00130
Keywords ascertainment, Down syndrome, logistic regression, Markov chain Monte Carlo method, prevalence
Public URL https://uwe-repository.worktribe.com/output/1082184
Publisher URL http://dx.doi.org/10.1111/1467-9876.00130