Regression-based variable clustering for data reduction

In many studies it is of interest to cluster states, counties or other small regions in order to obtain improved estimates of disease rates or other summary measures, and a more parsimonious representation of the country as a whole. This may be the case if there are too many to summarize concisely,...

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Bibliographic Details
Published in:Statistics in medicine Vol. 21; no. 6; pp. 921 - 941
Main Authors: McClelland, R. L., Kronmal, R. A.
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 30.03.2002
Wiley
Subjects:
Aged
Algorithms
Biological and medical sciences
Cardiovascular Diseases - epidemiology
Cluster Analysis
Cognition
Computer Simulation
Computerized, statistical medical data processing and models in biomedicine
data reduction
Humans
Infarction - epidemiology
Magnetic Resonance Imaging
Medical sciences
Medical statistics
Models, Statistical
regression
Regression Analysis
variable clustering
Cluster analysis
Human
Evaluation
Statistical analysis
Geographic distribution
Regression analysis
Statistics
Algorithm
Epidemiology
Medical application
Data reduction
ISSN:0277-6715, 1097-0258
Online Access:Get full text
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Summary:In many studies it is of interest to cluster states, counties or other small regions in order to obtain improved estimates of disease rates or other summary measures, and a more parsimonious representation of the country as a whole. This may be the case if there are too many to summarize concisely, and/or many regions with a small number of cases. By merging the regions into larger geographic areas, we obtain more cases within each area (and hence lower standard errors for parameter estimates), as well as fewer areas to summarize in terms of disease rates. The resulting clusters should be such that regions within the same cluster are similar in terms of their disease rates. In this paper we present a clustering algorithm which uses data at the subject‐specific level in order to cluster the original regions into a reduced set of larger areas. The proposed clustering algorithm expresses the clustering goals in terms of a regression framework. This formulation of the problem allows the regions to be clustered in terms of their association with the response, and confounding variables measured at the subject‐specific level may be easily incorporated during the clustering process. Additionally, this framework allows estimation and testing of the association between the areas and the response. The statistical properties and performance of the algorithm were evaluated via simulation studies, and the results are promising. Additional simulations illustrate the importance of controlling for confounding variables during the clustering process, rather than after the clusters are determined. The algorithm is illustrated with data from the Cardiovascular Health Study. Although developed with a specific application in mind, the method is applicable to a wide range of problems. Copyright © 2002 John Wiley & Sons, Ltd.
Bibliography:Georgetown Echo - No. RC-HL 35129 JHU MRI RC-HL 15103
istex:B390D1993EB7844F57590899DAD36C1C79276B3F
ark:/67375/WNG-Z8ZZ6FNV-N
ArticleID:SIM1063
National Heart, Lung and Blood Institute - No. N01-HC-85079; No. N01-HC-85086
ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0277-6715
1097-0258
DOI:10.1002/sim.1063