APPLICATION OF MARKOV-CHAIN MONTE-CARLO METHODS TO MODELING BIRTH PREVALENCE OF DOWN-SYNDROME

Data collected before the routine application of prenatal screening ar e of unique Value in estimating the natural live-birth prevalence of D own 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 r estricted to data sets in which ascertainment is assumed to be complet e. In this paper we adopt a Bayesian approach to modelling ascertainme nt and live-birth prevalence. We consider three prior specifications c oncerning ascertainment and compare predicted maternal-age-specific pr evalence under these three different prior specifications. The computa tions are carried out by using Markov chain Monte Carlo methods in whi ch model parameters and missing data are sampled.