Clustering Methods in Rheumatic and Musculoskeletal Disease Research: An Educational Guide to Best Research Practices
Samantha Chin, Jamie E. Collins
Abstract
Clinical manifestations and disease progression often exhibit significant variability among patients with rheumatic diseases, complicating diagnosis and treatment strategies. A better understanding of disease heterogeneity may allow for personalized treatment strategies. Cluster analysis is a class of statistical methods that aims to identify subgroups or patterns within a dataset. Cluster analysis is a type of unsupervised learning, meaning there are no outcomes or labels to guide the analysis (ie, there is no ground truth). This makes it difficult to assess the accuracy or validity of the identified clusters, and these methods therefore require thoughtful planning and careful interpretation. Here, we provide a high-level overview of clustering, including different types of clustering methods and important considerations when undertaking clustering, and review some examples from the rheumatology literature.