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  • Application of STATA network module for network meta-analysis of classification data

    Author(s)
    Zheng, Liang
    Lan, Qin
    Zhou, Xiao-hui
    Lin, Fang
    Sun, Jing
    Fan, Huimin
    Griffith University Author(s)
    Sun, Jing
    Year published
    2018
    Metadata
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    Abstract
    Network meta analysis (NMA) has received more and more attention in the field of evidence based medicine. NMA can quantitatively or qualitative reflect the emerging effect and compare the results through consistency, inconsistency assessment, relationship diagram forest plot and the correction funnel plot. This article introduced the installation of network module for the two categories of data in the STATA step by step: the installation of metan module, the installation of network module, other installation on methods of network module. The article used example to demonstrate how the NMA results are generated and how they ...
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    Network meta analysis (NMA) has received more and more attention in the field of evidence based medicine. NMA can quantitatively or qualitative reflect the emerging effect and compare the results through consistency, inconsistency assessment, relationship diagram forest plot and the correction funnel plot. This article introduced the installation of network module for the two categories of data in the STATA step by step: the installation of metan module, the installation of network module, other installation on methods of network module. The article used example to demonstrate how the NMA results are generated and how they are interpreted. Finally the implementation of other NMA results is also described.
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    Journal Title
    Journal of Tongji University (Medical Science)
    Volume
    39
    Issue
    3
    Publisher URI
    https://caod.oriprobe.com/articles/53973192/stata_ruan_jian_network_mo_kuai_zai_liang_fen_lei_.htm
    Subject
    Medical and Health Sciences not elsewhere classified
    Medical and Health Sciences
    Publication URI
    http://hdl.handle.net/10072/379977
    Collection
    • Journal articles

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