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Updated: Apr 28, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
On Comparison of Clustering Methods for Pharmacoepidemiological Data
Fanny Feuillet1, Lise Bellanger, Jean-Benoit Hardouin
1a EA 4275 SPHERE Biostatistics, Pharmacoepidemiology and Human Sciences Research , University of Nantes , Nantes , France.
Abstract:
The high consumption of psychotropic drugs is a public health problem. Rigorous statistical methods are needed to identify consumption characteristics in post-marketing phase. Agglomerative hierarchical clustering (AHC) and latent class analysis (LCA) can both provide clusters of subjects with similar characteristics. The objective of this study was to compare these two methods in pharmacoepidemiology, on several criteria: number of clusters, concordance, interpretation, and stability over time. From a dataset on bromazepam consumption, the two methods present a good concordance. AHC is a very stable method and it provides homogeneous classes. LCA is an inferential approach and seems to allow identifying more accurately extreme deviant behavior.
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