Identification of cancer-related symptom clusters: an empirical comparison of exploratory factor analysis methods.
BACKGROUND
Symptom clusters, important for symptom management strategies, have been determined empirically by various analytical methods. Guidance to select methods from the options available in standard statistical packages is limited.


OBJECTIVE
To compare alternative common factor analysis (FA) extraction methods appropriate to the data, to assess whether or not they determine similar symptom clusters, and to propose analytical approaches that are useful in this clinical context.


METHODS
Within one month of commencing chemotherapy, outpatients from oncology and hematology clinics (n = 202) reported their symptom experience on a modified Rotterdam Symptom Checklist. Symptom distress levels in the past week were rated on a scale of one (not at all) to four (very much). In a secondary data analysis of 42 symptoms, the associations between symptoms and factors were determined using alternative common FA methods: principal axis factoring, unweighted least squares, image factor analysis, and alpha factor analysis (AFA). Symptom inclusion in a cluster was based on the interpretation of pattern and structure coefficients, and importantly, clinical relevance of the grouping.


RESULTS
Five symptom clusters were commonly identified across methods: musculoskeletal discomforts/lethargy, oral discomforts, upper gastrointestinal discomforts, vasomotor symptoms, and gastrointestinal toxicities. In AFA, three additional clusters were lethargy, somatic symptoms, and treatment-related symptom clusters.


CONCLUSIONS
The most parsimonious solution resulted from principal axis factoring, but for large numbers of symptoms, AFA may be superior by identifying symptom clusters more useful for symptom management. Interpreting complex symptom relationships may lead to the investigation of pathophysiological mechanisms and intervention opportunities. Future studies should include psychological and cognitive symptoms.
Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. h.skerman@qut.edu.au
Helen M
HM
Skerman
Patsy M
PM
Yates
Diana
D
Battistutta
D005163:Factor Analysis, Statistical; D005767:Gastrointestinal Diseases / Q000150:complications* / Q000503:physiopathology; D006801:Humans; D053609:Lethargy / Q000150:complications* / Q000503:physiopathology; D059352:Musculoskeletal Pain / Q000150:complications* / Q000503:physiopathology; D009369:Neoplasms / Q000150:complications* / Q000503:physiopathology; D011788:Quality of Life; D012720:Severity of Illness Index; D013577:Syndrome