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Register a new userBayesian structural equation modeling for data from multiple cohorts
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Added by
Khue-Dung Dang
Publication date
2020
fields
Mathematical Statistics
and research's language is
English
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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.
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Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimers disease.
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