Designs for the combination of group- and individual-level data

Epidemiology. 2011 May;22(3):382-9. doi: 10.1097/EDE.0b013e3182125cff.

Abstract

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.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Bias*
  • Birth Weight
  • Confounding Factors, Epidemiologic
  • Data Collection
  • Data Interpretation, Statistical
  • Effect Modifier, Epidemiologic*
  • Epidemiologic Research Design*
  • Ethnic Groups / statistics & numerical data
  • Female
  • Humans
  • Infant, Low Birth Weight*
  • Infant, Newborn
  • Male
  • Models, Statistical
  • North Carolina
  • Risk Factors
  • Sex Factors