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Symptom clusters in patients with advanced cancer: a reanalysis comparing different statistical methods
Emily Chen1, Janet Nguyen, Luluel Khan
1Rapid Response Radiotherapy Program, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.
Journal of Pain and Symptom Management
|June 5, 2012
Summary
Statistical analysis methods significantly impact symptom cluster identification in advanced cancer patients. Utilizing a common analytical approach is crucial for consistent and clinically relevant symptom cluster research.
Area of Science:
- Oncology
- Biostatistics
- Psychometrics
Background:
- Clinical relevance of symptom cluster research is limited by inconsistencies.
- Varying statistical analyses contribute to these discrepancies.
Purpose of the Study:
- To assess the consistency of symptom cluster identification using three statistical methods (HCA, EFA, PCA).
- To examine temporal patterns of symptom clusters.
- To compare symptom clustering in radiotherapy responders and nonresponders over time.
Main Methods:
- Reanalysis of a dataset from 1296 advanced cancer patients.
- Application of hierarchical cluster analysis (HCA) and exploratory factor analysis (EFA) at multiple time points.
- Comparison with principal component analysis (PCA) results and subgroup analysis (responders vs. nonresponders).
Main Results:
- Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed higher correlation than exploratory factor analysis (EFA).
- No complete consensus among the three methods was achieved at any time point.
- Divergent symptom cluster patterns emerged between responders and nonresponders over time.
- Anxiety/depression and fatigue/drowsiness pairs remained consistently clustered.
Conclusions:
- Symptom cluster identification and composition are dependent on the statistical method employed.
- Adoption of a standardized analytical method is essential for consistency in symptom cluster research.
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