Skip to main content

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

Buy Article:

The full text article is temporarily unavailable.

We apologise for the inconvenience. Please try again later.

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.

Keywords: Ascertainment; Down syndrome; Logistic regression; Markov chain Monte Carlo method; Prevalence

Document Type: Research Article

Affiliations: University of Plymouth, UK

Publication date: January 1, 1998

bpl/rssc/1998/00000047/00000004/art00130
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more