Comments

Biometrics. 2007 Mar;63(1):128-36. http://www.blackwell-synergy.com/doi/full/10.1111/j.1541-0420.2006.00673.x

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 non-identifiability 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 paper is hierarchical models to account for between-group heterogeneity. Estimation and inference pose serious compu- tational 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.

Disciplines

Disease Modeling | Epidemiology