Overcoming biases and misconceptions in ecological studies

The aggregate data study design provides an alternative group level analysi s to ecological studies in the estimation of individual level health risks. An aggregate model is derived by aggregating a plausible individual level relative rate model within groups, such that population-based disease rates are modelled as functions of individual level covariate data. We apply an aggregate data method to a series of fictitious examples from a review pape r by Greenland and Robins which illustrated the problems that can arise whe n using the results of ecological studies to make inference about individua l health risks. We use simulated data based on their examples to demonstrat e that the aggregate data approach can address many of the sources of bias that are inherent in typical ecological analyses, even though the limited b etween-region covariate variation in these examples reduces the efficiency of the aggregate study. The aggregate method has the potential to estimate exposure effects of interest in the presence of non-linearity. confounding at individual and group levels, effect modification, classical measurement error in the exposure and non-differential misclassification in the confoun der.