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    Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R

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    Authors
    Zhang, Zhongheng
    Murtagh, Fionn cc
    Van Poucke, Sven
    Lin, Su
    Lan, Peng
    Affiliation
    Zhejiang University
    University of Derby
    Ziekenhuis Oost-Limburg
    Fujian Medical University
    Issue Date
    2017-02
    
    Metadata
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    Abstract
    Big data clinical research typically involves thousands of patients and there are numerous variables available. Conventionally, these variables can be handled by multivariable regression modeling. In this article, the hierarchical cluster analysis (HCA) is introduced. This method is used to explore similarity between observations and/or clusters. The result can be visualized using heat maps and dendrograms. Sometimes, it would be interesting to add scatter plot and smooth lines into the panels of the heat map. The inherent R heatmap package does not provide this function. A series of scatter plots can be created using lattice package, and then background color of each panel is mapped to the regression coefficient by using custom-made panel functions. This is the unique feature of the lattice package. Dendrograms and color keys can be added as the legend elements of the lattice system. The latticeExtra package provides some useful functions for the work.
    Citation
    Zhang Z, Murtagh F, Van Poucke S, Lin S, Lan P. Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R. Ann Transl Med 2017;5(4):75. doi: 10.21037/atm.2017.02.05
    Publisher
    AME Publishing Company
    Journal
    Annals of Translational Medicine
    URI
    http://hdl.handle.net/10545/621536
    DOI
    10.21037/atm.2017.02.05
    Additional Links
    http://atm.amegroups.com/article/view/13789/pdf
    http://atm.amegroups.com/article/view/13789/14063
    Type
    Article
    Language
    en
    ISSN
    23055847
    ae974a485f413a2113503eed53cd6c53
    10.21037/atm.2017.02.05
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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