title: A hybrid model for reducing ecological bias.
abstract: A major drawback of epidemiological ecological studies, in which the association between area-level summaries of risk and exposure is used to make inference about individual risk, is the difficulty in characterizing 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 nonparametric Dirichlet process prior with an estimating functions' approach and show that this model gives a compromise between 2 previously described methods. The method is investigated using simulated data, and a practical illustration is provided through an analysis of lung cancer mortality and residential radon exposure in counties of Minnesota. We conclude that we require good quality prior information about the exposure/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.
authors.affiliation: Department of Mathematical Sciences, University of Bath, Bath, UK. R.E.Salway@bath.ac.uk
authors.forename: Ruth
authors.initials: R
authors.lastname: Salway
authors.forename: Jon
authors.initials: J
authors.lastname: Wakefield
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004781:Environmental Exposure / Q000009:adverse effects; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology / Q000209:etiology; D008910:Minnesota / Q000453:epidemiology; D008954:Models, Biological; D015233:Models, Statistical; D009381:Neoplasms, Radiation-Induced / Q000453:epidemiology / Q000209:etiology; D011886:Radon / Q000506:poisoning
chemical_list: D011886:Radon