AI Article Synopsis
- * This article discusses using hierarchical models to address variability between groups, highlighting the computational difficulties in estimating and interpreting these models.
- * A Bayesian approach is introduced, incorporating a data augmentation technique where unobserved data acts as auxiliary variables, with practical examples drawn from infant mortality data in North Carolina.
Article Abstract
The ecological study design suffers from a broad range of biases that result from the loss of information regarding the joint distribution of individual-level outcomes, exposures, and confounders. The consequent nonidentifiability of individual-level models cannot be overcome without additional information; we combine ecological data with a sample of individual-level case-control data. The focus of this article is hierarchical models to account for between-group heterogeneity. Estimation and inference pose serious computational challenges. We present a Bayesian implementation based on a data augmentation scheme where the unobserved data are treated as auxiliary variables. The methods are illustrated with a dataset of county-specific infant mortality data from the state of North Carolina.
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| http://dx.doi.org/10.1111/j.1541-0420.2006.00673.x | DOI Listing |
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