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 that there are no outcomes or label to guide the analysis, i.e., there is no ground truth. This makes it difficult to assess the accuracy or validity of the identified clusters and therefore these methods require thoughtful planning and careful interpretation. Here we provide a high-level overview of clustering, including different types of clustering methods, important considerations when undertaking clustering, and review some examples from the rheumatology literature.

中文翻译：

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风湿病和肌肉骨骼疾病研究中的聚类方法：最佳研究实践教育指南。


风湿病患者的临床表现和疾病进展往往表现出显着的差异，使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性化治疗策略。聚类分析是一类统计方法，旨在识别数据集中的子组或模式。聚类分析是一种无监督学习，这意味着没有结果或标签来指导分析，即没有基本事实。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、进行聚类时的重要注意事项，并回顾了风湿病学文献中的一些示例。