Linked Life Data

16217847

Source:http://linkedlifedata.com/resource/pubmed/id/16217847

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
2006-5-17
pubmed:abstractText
In typical small-area studies of health and environment we wish to make inference on the relationship between individual-level quantities using aggregate, or ecological, data. Such ecological inference is often subject to bias and imprecision, due to the lack of individual-level information in the data. Conversely, individual-level survey data often have insufficient power to study small-area variations in health. Such problems can be reduced by supplementing the aggregate-level data with small samples of data from individuals within the areas, which directly link exposures and outcomes. We outline a hierarchical model framework for estimating individual-level associations using a combination of aggregate and individual data. We perform a comprehensive simulation study, under a variety of realistic conditions, to determine when aggregate data are sufficient for accurate inference, and when we also require individual-level information. Finally, we illustrate the methods in a case study investigating the relationship between limiting long-term illness, ethnicity and income in London.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2005 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2136-59
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Improving ecological inference using individual-level data.
pubmed:affiliation
Department of Epidemiology and Public Health, Imperial College School of Medicine, London, U.K. chris.jackson@imperial.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't