Distribution of the EQ-5D-5L Profiles and Values in Three Patient Groups [Editor's Choice]



The distribution of EQ-5D-3L values (health state profiles, weighted by value sets) often shows two distinct groups, arising from both the distribution of profiles and the characteristics of value sets. To date, there is little evidence about the distribution of EQ-5D-5L values.


To explore the distribution of EQ-5D-5L profiles; to compare the distributions of EQ-5D-5L values arising from the English value set (EVS) and a ‘mapped’ value set (MVS); and to develop further the methods used to investigate clustering within EQ-5D data.


We obtained data from Cambridgeshire Community Services NHS Trust containing EQ-5D-5L profiles before treatment for three patient groups: community rehabilitation (N=6919); musculoskeletal physiotherapy (N=19999); and specialist nursing services (N=3366). Values were calculated using the EVS and MVS. Clusters were examined using the k-means method and Calinski–Harabasz pseudo-F index stopping rule.


We found no evidence for clustering of EQ-5D-5L values arising from the classification system and no strong or consistent evidence of clustering arising from the EVS. There was clearer evidence of clustering using the MVS, with two being the optimal number of clusters. The clusters that were found for the EVS were very different from the MVS clusters.


Unlike the EQ-5D-3L, clustering of EQ-5D-5L values does not seem to be driven by clustering of its profile. This suggests the EQ-5D-5L is superior in that it is less likely to generate artefactual clusters – however, clusters may still result from using value sets such as MVS that have the tendency to generate them.


Yan Feng Nancy Devlin Andrew Bateman Bernarda Zamora David Parkin

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