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Regression-based variable clustering for data reduction.
Tuesday,2012-11-06 12:05 America/Los_Angeles — eenright
| Title | Regression-based variable clustering for data reduction. |
| Publication Type | Journal Article |
| Year of Publication | 2002 |
| Authors | McClelland, RL, Kronmal, RA |
| Journal | Stat Med |
| Volume | 21 |
| Issue | 6 |
| Pagination | 921-41 |
| Date Published | 2002 Mar 30 |
| ISSN | 0277-6715 |
| Keywords | Aged, Algorithms, Cardiovascular Diseases, Cluster Analysis, Cognition, Computer Simulation, Humans, Infarction, Magnetic Resonance Imaging, Models, Statistical, Regression Analysis |
| Abstract | <p>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.</p> |
| DOI | 10.1002/sim.1063 |
| Alternate Journal | Stat Med |
| PubMed ID | 11870825 |
| Grant List | N01-HC-85079 / HC / NHLBI NIH HHS / United States N01-HC-85080 / HC / NHLBI NIH HHS / United States N01-HC-85081 / HC / NHLBI NIH HHS / United States N01-HC-85082 / HC / NHLBI NIH HHS / United States N01-HC-85083 / HC / NHLBI NIH HHS / United States N01-HC-85084 / HC / NHLBI NIH HHS / United States N01-HC-85085 / HC / NHLBI NIH HHS / United States N01-HC-85086 / HC / NHLBI NIH HHS / United States |