A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence

Nikoloulopoulos, Aristidis K (2017) A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence. Statistical Methods in Medical Research, 26 (5). pp. 2270-2286. ISSN 1477-0334

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    Abstract

    A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context. Here, we call trivariate vine copulas to extend the bivariate meta-analysis of diagnostic test accuracy studies by accounting for disease prevalence. Our vine copula mixed model includes the trivariate generalized linear mixed model as a special case and can also operate on the original scale of sensitivity, specificity, and disease prevalence. Our general methodology is illustrated by re-analyzing the data of two published meta-analyses. Our study suggests that there can be an improvement on trivariate generalized linear mixed model in fit to data and makes the argument for moving to vine copula random effects models especially because of their richness, including reflection asymmetric tail dependence, and computational feasibility despite their three dimensionality.

    Item Type: Article
    Uncontrolled Keywords: copula models,diagnostic tests,multivariate meta-analysis,random effects models,prevalence,vines
    Faculty \ School: Faculty of Science > School of Computing Sciences
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    Depositing User: Pure Connector
    Date Deposited: 24 Jul 2015 23:49
    Last Modified: 25 Jul 2018 10:51
    URI: https://ueaeprints.uea.ac.uk/id/eprint/53718
    DOI: 10.1177/0962280215596769

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