The use of multivariate methods in the identification of subtypes of Alzheimer's disease: a comparison of principal components and cluster analysis

Armstrong, Richard A.; Wood, L.; Myers, D. and Smith, C.U.M. (1996). The use of multivariate methods in the identification of subtypes of Alzheimer's disease: a comparison of principal components and cluster analysis. Dementia and Geriatric Cognitive Disorders, 7 (4), pp. 215-220.

Abstract

Two contrasting multivariate statistical methods, viz., principal components analysis (PCA) and cluster analysis were applied to the study of neuropathological variations between cases of Alzheimer's disease (AD). To compare the two methods, 78 cases of AD were analyzed, each characterised by measurements of 47 neuropathological variables. Both methods of analysis revealed significant variations between AD cases. These variations were related primarily to differences in the distribution and abundance of senile plaques (SP) and neurofibrillary tangles (NFT) in the brain. Cluster analysis classified the majority of AD cases into five groups which could represent subtypes of AD. However, PCA suggested that variation between cases was more continuous with no distinct subtypes. Hence, PCA may be a more appropriate method than cluster analysis in the study of neuropathological variations between AD cases.

Publication DOI: https://doi.org/10.1159/000106882
Divisions: Life & Health Sciences > Optometry
Life & Health Sciences > Ophthalmic Research Group
Life & Health Sciences > Health Sciences
Uncontrolled Keywords: Alzheimer's disease,subtypes,multivariate analysis,principal components analysis,cluster analysis,neuropathological variables
Published Date: 1996

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