Advance Access published on June 16, 2007 for Biostatistics.


A major drawback of epidemiological ecological studies, in which the association between area-level summaries of risk and exposure are used to make inference about individual risk, is the difficulty in characterising within-area variability in exposure and confounder variables. To avoid ecological bias, samples of individual exposure/confounder data within each area are required. Unfortunately these may be difficult or expensive to obtain, particularly if large samples are required. In this paper we propose a new approach suitable for use with small samples. We combine a Bayesian non-parametric Dirichlet process prior with an estimating functions approach, and show that this model gives a compromise between two previously-described methods. The method is investigated using simulated data, and a practical illustration is provided through an analysis of mortality and income data across England. We conclude that we require good quality prior information about the expo- sure/confounder distributions and a large between- to within-area variability ratio for an ecological study to be feasible using only small samples of individual data.


Disease Modeling | Epidemiology