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Updated: Jun 25, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Multivariate methods to identify cancer-related symptom clusters.

Helen M Skerman¹, Patsy M Yates, Diana Battistutta

¹Institute of Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, Queensland 4059, Australia.

Research in Nursing & Health

|March 11, 2009

Summary

Multivariate methods like factor analysis (FA) are best for identifying cancer symptom clusters. Hierarchical cluster analysis (HCA) is less suitable, and principal component analysis is inappropriate for this research.

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Last Updated: Jun 25, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Oncology
Biostatistics
Psychometrics

Background:

Multivariate statistical methods are essential for understanding complex relationships between multiple concurrent symptoms.
Cancer symptom clusters require rigorous statistical approaches for accurate identification and management.

Purpose of the Study:

To evaluate the conceptual and contextual suitability of common multivariate methods for identifying cancer symptom clusters.
To guide the selection of appropriate statistical techniques for future cancer symptom research.

Main Methods:

A systematic literature search was conducted across Medline, CINAHL, and PsycINFO databases.
Inclusion criteria focused on English-language, cross-sectional studies published within 10 years prior to March 2007.
13 studies met the inclusion criteria for analysis.

Main Results:

Common factor analysis (FA) and hierarchical cluster analysis (HCA) were identified as conceptually appropriate methods.
Principal component analysis was deemed inappropriate for symptom cluster identification.
FA methods were considered more suitable than HCA for advancing symptom management strategies.

Conclusions:

Factor analysis (FA) methods, specifically principal axis or maximum likelihood factoring with oblique rotation and clinical interpretation, are recommended for robust cancer symptom cluster identification.
These findings provide a foundation for developing targeted and effective cancer symptom management interventions.