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药物流行病学数据聚类方法的比较

On Comparison of Clustering Methods for Pharmacoepidemiological Data.

作者信息

Feuillet Fanny, Bellanger Lise, Hardouin Jean-Benoit, Victorri-Vigneau Caroline, Sébille Véronique

机构信息

a EA 4275 SPHERE Biostatistics, Pharmacoepidemiology and Human Sciences Research , University of Nantes , Nantes , France.

出版信息

J Biopharm Stat. 2015;25(4):843-56. doi: 10.1080/10543406.2014.920855.

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.

摘要

精神药物的高消费量是一个公共卫生问题。需要采用严格的统计方法来确定上市后阶段的消费特征。凝聚层次聚类(AHC)和潜在类别分析(LCA)都可以提供具有相似特征的受试者聚类。本研究的目的是在药物流行病学中,从几个标准(聚类数量、一致性、解释和随时间的稳定性)比较这两种方法。从一个关于溴西泮消费的数据集来看,这两种方法呈现出良好的一致性。AHC是一种非常稳定的方法,它提供了同质的类别。LCA是一种推理方法,似乎能更准确地识别极端偏差行为。

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