@article {1528,
	title = {Neurologic correlates of infarction-like lesion location on magnetic resonance imaging in the cardiovascular health study},
	journal = {J. Stroke Cerebrovascular Disease},
	volume = {9},
	year = {2000},
	month = {2000-01-01},
	pages = {228},
	chapter = {218},
	abstract = {Objective: To evaluate and quantify the associations of infarction-like lesion location in the brain with cognitive and physical impairment in an elderly population. Methods: Data from magnetic resonance imaging (MRI) scans for 3647 Cardiovascular Health Study participants were analyzed. Associations between infarction-like lesion location and various neurologic and performance-based measurements were assessed by using regression models. Continuous responses were expressed in percentiles. Models excluding participants with a history of stroke were also examined. Results: Brainstem infarction-like lesions were associated with a severely reduced Mini-Mental State Examination score (difference [d] = 15 percentiles, P < .001), reduced digit-symbol score (d = 12 percentiles, P < .01), increased time to walk 15 feet (d = 15 percentiles, P < .001), and increased odds of a history of dizziness (odds ratio [OR] = 2.5, P <.01). Basal ganglia infarctions were associated with an increased prevalence of visual field deficits (OR = 1.8, P <.001), and cerebellar white matter infarction-like lesions with an increased prevalence of a history of coma (OR = 4.0, P < .01). For those with infarction-like lesions in both the cerebral cortex and brainstem, the degree of sleepiness was substantially elevated (d = 34 percentiles, P < .01). No associations with a history of migraines were detected. After exclusion of those with stroke history, all of these associations persisted. Conclusion: The estimated magnitude and statistical significance of these associations relative to those with no infarction-like lesions provides new information. Further investigation for some of the associations, such as the role of the brainstem in cognitive function and the association of basal ganglia infarction-like lesions with visual field deficits, would be worthwhile.},
	keywords = {basal, Brain, Cerebral Cortex, ganglia, Health, history, Infarction, Magnetic Resonance Imaging, methods, Odds Ratio, Population, Prevalence, Stroke},
	author = {McClelland, RL and Kronmal, RA and Bryan, RN and Manolio, TA and Herskovits, EH and Kuller, LH and O{\textquoteright}Leary, DH}
}
@article {681,
	title = {Regression-based variable clustering for data reduction.},
	journal = {Stat Med},
	volume = {21},
	year = {2002},
	month = {2002 Mar 30},
	pages = {921-41},
	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>},
	keywords = {Aged, Algorithms, Cardiovascular Diseases, Cluster Analysis, Cognition, Computer Simulation, Humans, Infarction, Magnetic Resonance Imaging, Models, Statistical, Regression Analysis},
	issn = {0277-6715},
	doi = {10.1002/sim.1063},
	author = {McClelland, R L and Kronmal, R A}
}
