Multivariate methods to identify cancer-related symptom clusters
Multivariate methods are required to assess the interrelationships among multiple, concurrent symptoms. We examined the conceptual and contextual appropriateness of commonly used multivariate methods for cancer symptom cluster identification. From 178 publications identified in an online database search of Medline, CINAHL, and PsycINFO, limited to articles published in English, 10 years prior to March 2007, 13 cross-sectional studies met the inclusion criteria. Conceptually, common factor analysis (FA) and hierarchical cluster analysis (HCA) are appropriate for symptom cluster identification, not principal component analysis. As a basis for new directions in symptom management, FA methods are more appropriate than HCA. Principal axis factoring or maximum likelihood factoring, the scree plot, oblique rotation, and clinical interpretation are recommended approaches to symptom cluster identification.
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|Item Type:||Journal Article|
|Keywords:||Symptom Clusters, Cancer, Symptoms, Multivariate, Factor Analysis, Cluster Analysis|
|Subjects:||Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > ONCOLOGY AND CARCINOGENESIS (111200) > Cancer Diagnosis (111202)|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Nursing
Current > Schools > School of Public Health & Social Work
|Copyright Owner:||Copyright 2010 Wiley Periodicals, Inc.|
|Deposited On:||04 Feb 2010 04:52|
|Last Modified:||29 Feb 2012 14:05|
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