S. Chin, BS, Orthopaedic and Arthritis Center for Outcomes Research, Brigham and Women's Hospital.
J.E. Collins, PhD, Orthopaedic and Arthritis Center for Outcomes Research, Brigham and Women's Hospital, and Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts, USA.
J Rheumatol. 2024 Dec 1;51(12):1160-1168. doi: 10.3899/jrheum.2024-0519.
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.
风湿性疾病患者的临床表现和疾病进展常存在显著差异，这使得诊断和治疗策略变得复杂。更好地了解疾病异质性可能有助于制定个体化的治疗策略。聚类分析是一类统计学方法，旨在识别数据集中的亚组或模式。聚类分析是一种无监督学习方法，这意味着没有结果或标签来指导分析（即没有真实情况）。这使得评估所识别的聚类的准确性或有效性变得困难，因此这些方法需要深思熟虑的规划和仔细的解释。在这里，我们提供了聚类的高级概述，包括不同类型的聚类方法以及进行聚类时的重要考虑因素，并回顾了一些来自风湿病文献的示例。
