title: Geographic-based ecological correlation studies using supplemental case-control data.
abstract: It is well known that the ecological study design suffers from a variety of biases that render the interpretation of its results difficult. Despite its limitations, however, the ecological study design is still widely used in a range of disciplines. The only solution to the ecological inference problem is to supplement the aggregate data with individual-level data and, to this end, Haneuse and Wakefield (Biometrics 2007; 63:128-136) recently proposed a hybrid study design in which an ecological study is supplemented with a sample of case-control data. The latter provides the basis for the control of bias, while the former may provide efficiency gains. Building on that work, we illustrate the use of the hybrid design in the context of a geographical correlation study of lung cancer mortality from the state of Ohio. Focusing on epidemiological applications, we initially provide an overview of the use of ecological studies in scientific research, highlighting the breadth of current application as well as advantages and drawbacks of the design. We consider the interplay between the two sources of information in the design: ecological and case-control, and then provide details on a Bayesian spatial random effects model in the setting of the hybrid design. Issues of specification are addressed, as well as sensitivity to modeling assumptions. Further, an interesting feature of these data is that they provide an example of how the proposed design may be used to resolve the ecological fallacy.
authors.affiliation: Center for Health Studies, Group Health Cooperative, Seattle, WA 98101, USA. haneuse.s@ghc.org
authors.forename: S
authors.initials: S
authors.lastname: Haneuse
authors.forename: J
authors.initials: J
authors.lastname: Wakefield
mesh_terms: D001499:Bayes Theorem; D016022:Case-Control Studies; D004463:Ecology / Q000706:statistics & numerical data*; D004787:Environmental Pollution / Q000009:adverse effects; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000401:mortality; D008297:Male; D009820:Ohio / Q000453:epidemiology; D012107:Research Design; D017062:Small-Area Analysis