Document Detail


Regression of area mortality rates on explanatory variables: what weighting is appropriate?
MedLine Citation:
PMID:  12157995     Owner:  PIP     Status:  MEDLINE    
Abstract/OtherAbstract:
"One can often gain insight into the aetiology of a disease by relating mortality rates in different areas to explanatory variables. Multiple regression techniques are usually employed, but unweighted least squares may be inappropriate if the areas vary in population size. Also, a fully weighted regression, with weights inversely proportional to binomial sampling variances, is usually too extreme. This paper proposes an intermediate solution via maximum likelihood which takes account of three sources of variation in death rates: sampling error, explanatory variables and unexplained differences between areas. The method is also adapted for logit (death rates), standardized mortality ratios (SMRs) and log (SMRs). Two [United Kingdom] examples are presented."
Authors:
S J Pocock; D G Cook; S A Beresford
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of the Royal Statistical Society. Series C, Applied statistics     Volume:  30     ISSN:  0035-9254     ISO Abbreviation:  J R Stat Soc Ser C Appl Stat     Publication Date:  1981  
Date Detail:
Created Date:  1983-12-05     Completed Date:  1983-12-05     Revised Date:  2007-11-15    
Medline Journal Info:
Nlm Unique ID:  101086541     Medline TA:  J R Stat Soc Ser C Appl Stat     Country:  England    
Other Details:
Languages:  eng     Pagination:  286-95     Citation Subset:  J    
Copyright Information:
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MeSH Terms
Descriptor/Qualifier:
Demography
Developed Countries
Europe
Great Britain
Mortality*
Population
Population Dynamics
Regression Analysis*
Research
Statistics as Topic

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


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