Symptom clusters in a population-based ambulatory cancer cohort validated using bootstrap methods

BACKGROUND: Cluster identification has emerged as a priority for symptom research. Variation in statistical approaches has hampered the identification of common clusters that should be targeted for intervention. The purpose of this study was to identify and validate common symptom clusters in a large population-based cohort of ambulatory cancer subjects. METHODS: This descriptive, factor analysis study used bootstrap methods to derive a stable factor structure to identify symptom clusters in a population-based sample of cancer patients. Subjects were identified from a provincial symptom database and linked to other provincial databases. Symptom clusters were validated using confirmatory factor analysis in a randomly selected portion of the sample and model fit examined using common goodness of fit criteria. RESULTS: The cluster cohort included 14,247 subjects. Three symptom clusters were identified: fatigue-sickness symptoms (tiredness, nausea, drowsiness and shortness of breath), emotional distress (depression and anxiety), and a poor sense of well-being (appetite and well-being). These clusters were stable across most sub-populations in the cohort. CONCLUSION: The identification of common symptom clusters using robust statistical methods will help to yield targets to improve symptom management and identify populations at risk for worse disease outcomes.