Fig. 3.

Hierarchical cluster analysis (HCA) - Dendrogram (a) and Intertia jump curve (b) for state sequences by week. a Patients with similar sum of dimension-specific distances were classified in the same group. In HCA, each patient starts in its own cluster, and then pairs of clusters are merged as one moves up the hierarchy, until all patients are combined in a unique group. The Ward’s linkage criterion was chosen to find the pair of clusters that leads to minimum increase in total within-cluster variance after merging. b The choice of the optimal number of groups or clusters was guided on statistical criteria (sum of squares or inertia)