Purpose of review: Within a broader perspective on the next challenges in oncologic symptom cluster research, the objectives of this review are to examine the statistical methods that have been used to quantify and/or model the dynamic nature of symptom clustering, the methodological issues associated with those methods, and the statistical modeling techniques for the underlying mechanisms of symptom clustering.
Recent findings: Correlation, factor analysis, principal component analysis, and cluster analysis are analytical methods to identify symptom clusters and/or to examine the influence of symptom clusters on patient outcomes. More recent techniques include latent variable methods, such as latent profile analysis, to examine the phenotypes of symptom cluster experience and growth modeling to examine the longitudinal nature of symptom cluster experience. Future endeavors include an investigation of the underlying mechanisms of symptom clustering using longitudinal data analysis. The methodological issues include the domain of the symptoms, measurement errors, stability of the solution within the data, measurement timing, and sample size.
Summary: Each method has unique strengths and weaknesses, and the method choice should be driven by the aims and research questions of a given study.