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      Meta-evaluation of meta-analysis: ten appraisal questions for biologists

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          Abstract

          Meta-analysis is a statistical procedure for analyzing the combined data from different studies, and can be a major source of concise up-to-date information. The overall conclusions of a meta-analysis, however, depend heavily on the quality of the meta-analytic process, and an appropriate evaluation of the quality of meta-analysis (meta-evaluation) can be challenging. We outline ten questions biologists can ask to critically appraise a meta-analysis. These questions could also act as simple and accessible guidelines for the authors of meta-analyses. We focus on meta-analyses using non-human species, which we term ‘biological’ meta-analysis. Our ten questions are aimed at enabling a biologist to evaluate whether a biological meta-analysis embodies ‘mega-enlightenment’, a ‘mega-mistake’, or something in between.

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          Most cited references131

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          Measuring inconsistency in meta-analyses.

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            Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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              Bias in meta-analysis detected by a simple, graphical test

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                Author and article information

                Contributors
                s.nakagawa@unsw.edu.au
                Journal
                BMC Biol
                BMC Biol
                BMC Biology
                BioMed Central (London )
                1741-7007
                3 March 2017
                3 March 2017
                2017
                : 15
                : 18
                Affiliations
                [1 ]ISNI 0000 0004 4902 0432, GRID grid.1005.4, , Evolution & Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, ; Sydney, NSW 2052 Australia
                [2 ]ISNI 0000 0000 9983 6924, GRID grid.415306.5, , Diabetes and Metabolism Division, Garvan Institute of Medical Research, ; 384 Victoria Street, Darlinghurst, Sydney, NSW 2010 Australia
                [3 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, , Charles Perkins Centre, University of Sydney, ; Sydney, NSW 2006 Australia
                [4 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, , School of Mathematics and Statistics, University of Sydney, ; Sydney, NSW 2006 Australia
                Article
                357
                10.1186/s12915-017-0357-7
                5336618
                28257642
                72d33e92-002e-4659-93be-ff47edca7aab
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                © The Author(s) 2017

                Life sciences
                effect size,biological importance,non-independence,meta-regression,meta-research,publication bias,quantitative synthesis,reporting bias,statistical significance,systematic review

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