Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada.
Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
J Clin Epidemiol. 2024 Aug;172:111435. doi: 10.1016/j.jclinepi.2024.111435. Epub 2024 Jun 18.
To examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters.
Using cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians.
Results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from -0.02 to 0.24, indicating little similarity.
These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.
当进行网络分析（聚类方法和关联度量）时，研究两个关键选择对多种合并症聚类的数量和类型的影响。
利用来自加拿大老龄化纵向研究的 30097 名 45-85 岁社区居住成年人的 24 种疾病的横断面自我报告数据，我们使用了 5 种聚类方法和 11 种在多种合并症研究中常用的关联度量方法进行了网络分析。我们使用调整后的兰德指数（ARI）来比较聚类之间的相似性；ARI 为 0 相当于将疾病随机分配到聚类中，而 1 表示完全一致。我们将网络分析结果与两位临床医生独立确定的疾病聚类进行了比较。
关联度量和聚类算法的组合结果差异很大。识别出的聚类数量从 1 到 24 不等，聚类内部的条件相似性较低。与临床医生确定的聚类相比，ARI 范围从-0.02 到 0.24，表明相似性较低。
这些分析表明需要对类似疾病的二元聚类数据的网络分析方法的性能进行系统评估。此外，在个体老年人中，疾病可能不会可预测地聚类，这突出了个性化护理的必要性。
