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Table 4 SUBGROUPS, person-based hypotheses

From: Analyzing repeated data collected by mobile phones and frequent text messages. An example of Low back pain measured weekly for 18 weeks

Research question Outcome Method of analysis Results from the model data set
All respondents, n = 244 Respondents answering ≥ 80%, n = 161 Respondents answering all first 8 weeks, n = 133
5: Are there subgroups of patients? Subgroups as clusters with low within-cluster variation and high between-cluster variation A. Visual inspection based on plots of the course of pain in a graphical presentation where predefined criteria of directions in early and late phases, a qualitative approach A: Illustrated in Table 5 Not applied Not applied
   B: Regression coefficients from spline regression (1 knot) derived from each subject were used in Wards’ hierarchical cluster analysis. Optionally this analysis was followed by K-means cluster analysis. Inspection of number of clusters based on the Calinski-Harabasz criterion and the criteria by Duda & Hart B: Not done due to lack of degrees of freedom in spline regression of some individual subjects B: 4 clusters suggested. Not applied
   C: Wards’ hierarchical cluster analysis, optionally followed by K-means cluster analysis, applied directly on the weekly number of pain days for the first 8 weeks. Cluster criteria as in B. Cluster 2: 79 Percentage with short duration in these clusters: C: 6 clusters suggested. Percentage with short duration the previous year in these clusters:
    C: Not applied Cluster 1: 39 Cluster 1: 58
     Cluster 2: 49 Cluster 3: 20
     Cluster 3: 85 Cluster 4: 33
     Cluster 4: 37 Cluster 5: 52
     C: Not applied Cluster 6: 50