Document Detail

Avoiding bias from aggregate measures of exposure.
MedLine Citation:
PMID:  17435216     Owner:  NLM     Status:  MEDLINE    
BACKGROUND: Sometimes in descriptive epidemiology or in the evaluation of a health intervention policy change, proportions exposed to a risk factor or to an intervention are used as explanatory variables in log-linear regressions for disease incidence or mortality.
AIM: To demonstrate how estimates from such models can be substantially inaccurate as estimates of the effect of the risk factor or intervention at individual level. To show how the individual level effect can be correctly estimated by excess relative risk models.
METHODS: The problem and solution are demonstrated using data on prostate-specific antigen testing and prostate cancer incidence.
Stephen W Duffy; Håkan Jonsson; Olorunsola F Agbaje; Nora Pashayan; Rhian Gabe
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of epidemiology and community health     Volume:  61     ISSN:  0143-005X     ISO Abbreviation:  J Epidemiol Community Health     Publication Date:  2007 May 
Date Detail:
Created Date:  2007-04-16     Completed Date:  2007-07-10     Revised Date:  2013-06-06    
Medline Journal Info:
Nlm Unique ID:  7909766     Medline TA:  J Epidemiol Community Health     Country:  England    
Other Details:
Languages:  eng     Pagination:  461-3     Citation Subset:  IM    
Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Charterhouse Square, London EC1M 6BQ, UK.
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MeSH Terms
Bias (Epidemiology)*
Logistic Models
Risk Assessment

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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