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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.

Purpose To construct empirically a conversion table to convert performance status scores among the Eastern Cooperative Oncology Group (ECOG), Karnofsky Performance Status (KPS) and Palliative Performance Scale (PPS) measures, using a large sample of patients with advanced cancer. Methods Seven physicians completed assessments on 1385 consecutive patients attending an oncology palliative care clinic, or admitted to an acute cancer palliative care unit. The three measures were distributed as a questionnaire package; the order in which they were presented was randomly assigned for each week. Scales were compared using the hit rate and the weighted kappa coefficient ( κ w ). The KPS and PPS were compared directly; for comparisons of either scale with the ECOG, all 70 possible categorisations of KPS and PPS were computed. An ‘ideal’ categorisation was selected based on maximisation of both statistical methods. Results The KPS and PPS matched in 1209 out of 1385 assessments (hit rate 87%; κ w 0.97). For both the KPS and the PPS, the categorisation of 100 (ECOG 0), 80–90 (1), 60–70 (2), 40–50 (3), 10–30 (4) had the highest hit rate (75%), and the second highest κ w (0.84, p κ w (0.85 for both KPS and PPS), but a lower hit rate (73% for KPS, 72% for PPS). Conclusions We have derived empirically a conversion scale among the ECOG, KPS and PPS scales. The proposed scale provides a means of translating amongst these measures, which may improve accuracy of communication about performance status amongst oncology clinicians and researchers.