| Institution: | 1. School of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010 Australia;2. School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW 2007 Australia
Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, Parkville, VIC 3010 Australia;3. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1 Canada;4. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213 USA;5. Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA 30322 USA;6. Seattle Children's Research Institute, Seattle, WA 98105 USA;7. Wayne State University School of Medicine, Detroit, MI 48201 USA |
| Abstract: | High levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, but the exact nature of the dose-response relationship is less well understood. To investigate this relationship, data were assembled from six longitudinal birth cohort studies examining the effects of PAE on cognitive outcomes from early school age through adolescence. Structural equation models (SEMs) are a natural approach to consider, because of the way they conceptualise multiple observed outcomes as relating to an underlying latent variable of interest, which can then be modelled as a function of exposure and other predictors of interest. However, conventional SEMs could not be fitted in this context because slightly different outcome measures were used in the six studies. In this paper we propose a multi-group Bayesian SEM that maps the unobserved cognition variable to a broad range of observed outcomes. The relation between these variables and PAE is then examined while controlling for potential confounders via propensity score adjustment. By examining different possible dose-response functions, the proposed framework is used to investigate whether there is a threshold PAE level that results in minimal cognitive deficit. |
