BACKGROUND: Advanced cancer patients often experience multiple concurrent symptoms, which can have prognostic effects on patients' quality of life. Including patients who did not experience all of the symptoms measured by an assessment tool may interfere with accurate symptom cluster identification. Varying statistical methods may also contribute to inconsistencies of cluster results. AIMS: To compare symptom clusters in a subgroup of patients reporting exclusively non-zero ESAS scores with those in the total patient sample. To examine whether using different statistical methods results in varied symptom clusters. DESIGN: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Exploratory Factor Analysis (EFA) were performed on the 'non-zero' subgroup and the total patient sample to identify symptom clusters at baseline and weeks 1, 2, 4, 8 and 12 following palliative radiotherapy. SETTING/PARTICIPANTS: A previous single-centre study used Principal Component Analysis to explore symptom clusters in 1296 advanced cancer patients. The present study analyzed this previously reported data set. RESULTS: Notably different symptom clusters were extracted between the two patient groups regardless of the statistical method at baseline, with the exception of a cluster composed of drowsiness, fatigue and dyspnea using Principal Component Analysis and Hierarchical Cluster Analysis. At follow-ups, different statistical methods yielded significantly varied symptom clusters. Only anxiety, depression and well-being consistently occurred in the same cluster across methods and over time. CONCLUSIONS: The composition of symptom clusters varied depending on if patients with non-zero scores were excluded at baseline and on the statistical method employed. Identifying valid clusters may prove useful for bettering symptom diagnosis and management for cancer patients.
BACKGROUND: Advanced cancerpatients often experience multiple concurrent symptoms, which can have prognostic effects on patients' quality of life. Including patients who did not experience all of the symptoms measured by an assessment tool may interfere with accurate symptom cluster identification. Varying statistical methods may also contribute to inconsistencies of cluster results. AIMS: To compare symptom clusters in a subgroup of patients reporting exclusively non-zero ESAS scores with those in the total patient sample. To examine whether using different statistical methods results in varied symptom clusters. DESIGN: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Exploratory Factor Analysis (EFA) were performed on the 'non-zero' subgroup and the total patient sample to identify symptom clusters at baseline and weeks 1, 2, 4, 8 and 12 following palliative radiotherapy. SETTING/PARTICIPANTS: A previous single-centre study used Principal Component Analysis to explore symptom clusters in 1296 advanced cancerpatients. The present study analyzed this previously reported data set. RESULTS: Notably different symptom clusters were extracted between the two patient groups regardless of the statistical method at baseline, with the exception of a cluster composed of drowsiness, fatigue and dyspnea using Principal Component Analysis and Hierarchical Cluster Analysis. At follow-ups, different statistical methods yielded significantly varied symptom clusters. Only anxiety, depression and well-being consistently occurred in the same cluster across methods and over time. CONCLUSIONS: The composition of symptom clusters varied depending on if patients with non-zero scores were excluded at baseline and on the statistical method employed. Identifying valid clusters may prove useful for bettering symptom diagnosis and management for cancerpatients.