Symptom clusters for revising scale membership in the analysis of prostate cancer patient reported outcome measures: a secondary data analysis of the Medical Research Council RT01 trial (ISCRTN47772397)

Abstract

PurposeTo investigate the role of symptom clusters in the analysis and utilisation of patient reported outcome measures (PROMs) for data modelling and clinical practice. To compare symptom clusters with scales, and to explore their value in PROMs interpretation and symptom management.MethodsA dataset called RT01 (ISCRTN47772397) of 843 prostate cancer patients was used. PROMs were reported with the University of California, Los Angeles Prostate Cancer Index (UCLA-PCI). Symptom clusters were explored with hierarchical cluster analysis (HCA) and average linkage method (correlation > 0.6). The reliability of the Urinary Function Scale was evaluated with Cronbach’s Alpha. The strength of the relationship between the items was investigated with Spearman’s correlation. Predictive accuracy of the clusters was compared to the scales by receiver operating characteristic (ROC) analysis. Presence of urinary symptoms at 3 years measured with the late effects on normal tissue: subjective, objective, management tool (LENT/SOM) was an endpoint.ResultsTwo symptom clusters were identified (urinary cluster and sexual cluster). The grouping of symptom clusters was different than UCLA-PCI Scales. Two items of the urinary function scales (“number of pads” and “urinary leak interfering with sex”) were excluded from the urinary cluster. The correlation with the other items in the scale ranged from 0.20 to 0.21 and 0.31 to 0.39, respectively. Cronbach’s Alpha showed low correlation of those items with the Urinary Function Scale (0.14–0.36 and 0.33–0.44, respectively). All urinary function scale items were subject to a ceiling effect. Clusters had better predictive accuracy, AUC = 0.70 –0.65, while scales AUC = 0.67–0.61.ConclusionThis study adds to the knowledge on how cluster analysis can be applied for the interpretation and utilisation of PROMs. We conclude that multiple-item scales should be evaluated and that symptom clusters provide a study-specific approach for modelling and interpretation of PROMs.