Symptom Clusters in Patients With Advanced Cancer: A Reanalysis Comparing Different Statistical Methods

Abstract

ContextThe clinical relevance of symptom cluster research remains questionable if inconsistencies, partially attributable to the varying statistical analyses used, exist. ObjectivesTo investigate whether symptom clusters identified were consistent using three different statistical methods and to observe the temporal pattern of clusters. A secondary objective was to compare symptom clustering in responders and nonresponders to radiotherapy over time. MethodsReanalysis of an existing data set compiled from 1296 patients with advanced cancer was performed using hierarchical cluster analysis (HCA) and exploratory factor analysis (EFA) to extract symptom clusters at baseline, 1-, 2-, 4-, 8-, and 12-week follow-up time points. Findings were compared with results obtained using principal component analysis (PCA) in our previously published study. The original sample was further divided into two subgroups: responders and nonresponders. The symptom clusters present in each subgroup were examined using PCA, HCA, and EFA at the same time points as mentioned above. ResultsThe symptom cluster findings of HCA and PCA correlated more frequently with each other than either did with the results of EFA. Complete consensus in all three statistical methods was never reached at any assessment time point in the present study. Increasingly diverging patterns of symptom cluster development over time were observed in the responder vs. nonresponder subgroups. Symptom pairs comprising anxiety and depression or fatigue and drowsiness consistently presented in the same cluster despite the shifting of other symptoms in the cluster over time. ConclusionThe presence and composition of symptom clusters identified varied depending on which statistical analysis method was used. A key step in achieving consistency in symptom cluster research involves the utilization of a common analytical method.