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Designs for the Combination of Group- and Individual-level Data

Haneuse, Sebastiena; Bartell, Scottb

doi: 10.1097/EDE.0b013e3182125cff
Methods: Review Article

Background: Studies of ecologic or aggregate data suffer from a broad range of biases when scientific interest lies with individual-level associations. To overcome these biases, epidemiologists can choose from a range of designs that combine these group-level data with individual-level data. The individual-level data provide information to identify, evaluate, and control bias, whereas the group-level data are often readily accessible and provide gains in efficiency and power. Within this context, the literature on developing models, particularly multilevel models, is well-established, but little work has been published to help researchers choose among competing designs and plan additional data collection.

Methods: We review recently proposed “combined” group- and individual-level designs and methods that collect and analyze data at 2 levels of aggregation. These include aggregate data designs, hierarchical related regression, two-phase designs, and hybrid designs for ecologic inference.

Results: The various methods differ in (i) the data elements available at the group and individual levels and (ii) the statistical techniques used to combine the 2 data sources. Implementing these techniques requires care, and it may often be simpler to ignore the group-level data once the individual-level data are collected. A simulation study, based on birth-weight data from North Carolina, is used to illustrate the benefit of incorporating group-level information.

Conclusions: Our focus is on settings where there are individual-level data to supplement readily accessible group-level data. In this context, no single design is ideal. Choosing which design to adopt depends primarily on the model of interest and the nature of the available group-level data.


From the aDepartment of Biostatistics, Harvard School of Public Health, Boston, MA; and bDepartment of Epidemiology and Program in Public Health, University of California at Irvine, Irvine, CA.

Submitted 13 April 2010; accepted 19 November 2010.

Supported, in part, by NCI R-01 grant CA125081.

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Correspondence: Sebastien Haneuse, Department of Biostatistics, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115. E-mail:

© 2011 Lippincott Williams & Wilkins, Inc.