  While it is well known that high levels of prenatal alcohol exposure (PAE)
result in significant cognitive deficits in children, the exact nature of the
dose response is less well understood. In particular, there is a pressing need
to identify the levels of PAE associated with an increased risk of clinically
significant adverse effects. To address this issue, data have been combined
from six longitudinal birth cohort studies in the United States that assessed
the effects of PAE on cognitive outcomes measured from early school age through
adolescence. Structural equation models (SEMs) are commonly used to capture the
association among multiple observed outcomes in order to characterise the
underlying variable of interest (in this case, cognition) and then relate it to
PAE. However, it was not possible to apply classic SEM software in our context
because different outcomes were measured in the six studies. In this paper we
show how a Bayesian approach can be used to fit a multi-group multi-level
structural model that maps cognition to a broad range of observed variables
measured at multiple ages. These variables map to several different cognitive
subdomains and are examined in relation to PAE after adjusting for confounding
using propensity scores. The model also tests the possibility of a change point
in the dose-response function.
