Open AccessDesigns for the Combination of Group- and Individual-level Data
Author(s) -
Sebastien Haneuse,
Scott M. Bartell
Publication year - 2011
Publication title -
epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.901
H-Index - 173
eISSN - 1531-5487
pISSN - 1044-3983
DOI - 10.1097/ede.0b013e3182125cff
Subject(s) - computer science , context (archaeology) , aggregate (composite) , data collection , aggregate data , multilevel model , inference , data mining , range (aeronautics) , data science , statistics , machine learning , artificial intelligence , mathematics , engineering , paleontology , materials science , composite material , biology , aerospace engineering
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.