Meta-Analysis for Evidence Synthesis in Plant Pathology: An Overview
Meta-analysis is the analysis of the results of multiple studies, which is typically performed in order to synthesize evidence from many possible sources in a formal probabilistic manner. In a simple sense, the outcome of each study becomes a single observation in the meta-analysis of all available...
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Veröffentlicht in: | Phytopathology 2011, Vol.101 (1), p.16-30 |
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description | Meta-analysis is the analysis of the results of multiple studies, which is typically performed in order to synthesize evidence from many possible sources in a formal probabilistic manner. In a simple sense, the outcome of each study becomes a single observation in the meta-analysis of all available studies. The methodology was developed originally in the social sciences by Smith, Glass, Rosenthal, Hunter, and Schmidt, based on earlier pioneering contributions in statistics by Fisher, Pearson, Yates, and Cochran, but this approach to research synthesis has now been embraced within many scientific disciplines. However, only a handful of articles have been published in plant pathology and related fields utilizing meta-analysis. After reviewing basic concepts and approaches, methods for estimating parameters and interpreting results are shown. The advantages of meta-analysis are presented in terms of prediction and risk analysis, and the high statistical power that can be achieved for detecting significant effects of treatments or significant relationships between variables. Based on power considerations, the fallacy of naïve counting of P values in a narrative review is demonstrated. Although there are many advantages to meta-analysis, results can be biased if the analysis is based on a nonrepresentative sample of study outcomes. Therefore, novel approaches for characterizing the upper bound on the bias are discussed, in order to show the robustness of meta-analysis to possible violation of assumptions. |
doi_str_mv | 10.1094/PHYTO-03-10-0069 |
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Based on power considerations, the fallacy of naïve counting of P values in a narrative review is demonstrated. Although there are many advantages to meta-analysis, results can be biased if the analysis is based on a nonrepresentative sample of study outcomes. Therefore, novel approaches for characterizing the upper bound on the bias are discussed, in order to show the robustness of meta-analysis to possible violation of assumptions.</description><identifier>ISSN: 0031-949X</identifier><identifier>EISSN: 1943-7684</identifier><identifier>DOI: 10.1094/PHYTO-03-10-0069</identifier><identifier>PMID: 21142781</identifier><identifier>CODEN: PHYTAJ</identifier><language>eng</language><publisher>St. Paul, MN: American Phytopathological Society</publisher><subject>accuracy ; Biological and medical sciences ; data analysis ; Data Interpretation, Statistical ; Fundamental and applied biological sciences. 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In a simple sense, the outcome of each study becomes a single observation in the meta-analysis of all available studies. The methodology was developed originally in the social sciences by Smith, Glass, Rosenthal, Hunter, and Schmidt, based on earlier pioneering contributions in statistics by Fisher, Pearson, Yates, and Cochran, but this approach to research synthesis has now been embraced within many scientific disciplines. However, only a handful of articles have been published in plant pathology and related fields utilizing meta-analysis. After reviewing basic concepts and approaches, methods for estimating parameters and interpreting results are shown. The advantages of meta-analysis are presented in terms of prediction and risk analysis, and the high statistical power that can be achieved for detecting significant effects of treatments or significant relationships between variables. Based on power considerations, the fallacy of naïve counting of P values in a narrative review is demonstrated. Although there are many advantages to meta-analysis, results can be biased if the analysis is based on a nonrepresentative sample of study outcomes. Therefore, novel approaches for characterizing the upper bound on the bias are discussed, in order to show the robustness of meta-analysis to possible violation of assumptions.</description><subject>accuracy</subject><subject>Biological and medical sciences</subject><subject>data analysis</subject><subject>Data Interpretation, Statistical</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>literature reviews</subject><subject>meta-analysis</subject><subject>Meta-Analysis as Topic</subject><subject>methodology</subject><subject>Models, Statistical</subject><subject>Models, Theoretical</subject><subject>Phytopathology. Animal pests. Plant and forest protection</subject><subject>Plant Diseases - statistics & numerical data</subject><subject>plant diseases and disorders</subject><subject>plant pathology</subject><subject>Plants - microbiology</subject><subject>prediction</subject><subject>Publication Bias</subject><subject>Research Design</subject><subject>risk assessment</subject><subject>statistical analysis</subject><issn>0031-949X</issn><issn>1943-7684</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkM1LwzAYxoMobk7vnrQX8RR989E08Tbm_ABlAxX0VNI00UrXzqSb7L-3dVOPnl54-D0PLz-EDgmcEVD8fHrz8jjBwDABDCDUFuoTxRlOhOTbqA_ACFZcPffQXgjvAJDIWOyiHiWE00SSPrq8t43Gw0qXq1CEyNU-Gi-L3FbGRg-rqnmzXVxU0bTUVRNNdfNWl_Xr6iIaVtFkaf2ysJ_7aMfpMtiDzR2gp6vx4-gG302ub0fDO2w4FQ0WceYyaWSWU5U5yUgGXCeMC-o44yqPJedgYy5BUuWkokxKlxuTxRlTJqFsgE7Xu3NffyxsaNJZEYwt29dsvQipjAlPqCTkf5LSmDMhoCVhTRpfh-CtS-e-mGm_SgmkneT0W3IKrAs6yW3laDO-yGY2_y38WG2Bkw2gg9Gl87oyRfjjmCIipt3Q8Zpzuk71q2-ZpwcKhAFRLAEm2Bcwmowk</recordid><startdate>2011</startdate><enddate>2011</enddate><creator>Madden, L.V</creator><creator>Paul, P.A</creator><general>American Phytopathological Society</general><scope>FBQ</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope></search><sort><creationdate>2011</creationdate><title>Meta-Analysis for Evidence Synthesis in Plant Pathology: An Overview</title><author>Madden, L.V ; Paul, P.A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-65bfb8c8bd29bf831b04a73462f4349d58440e5480829f892388fdccb5b39c723</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>accuracy</topic><topic>Biological and medical sciences</topic><topic>data analysis</topic><topic>Data Interpretation, Statistical</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>literature reviews</topic><topic>meta-analysis</topic><topic>Meta-Analysis as Topic</topic><topic>methodology</topic><topic>Models, Statistical</topic><topic>Models, Theoretical</topic><topic>Phytopathology. Animal pests. Plant and forest protection</topic><topic>Plant Diseases - statistics & numerical data</topic><topic>plant diseases and disorders</topic><topic>plant pathology</topic><topic>Plants - microbiology</topic><topic>prediction</topic><topic>Publication Bias</topic><topic>Research Design</topic><topic>risk assessment</topic><topic>statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Madden, L.V</creatorcontrib><creatorcontrib>Paul, P.A</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Phytopathology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Madden, L.V</au><au>Paul, P.A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Meta-Analysis for Evidence Synthesis in Plant Pathology: An Overview</atitle><jtitle>Phytopathology</jtitle><addtitle>Phytopathology</addtitle><date>2011</date><risdate>2011</risdate><volume>101</volume><issue>1</issue><spage>16</spage><epage>30</epage><pages>16-30</pages><issn>0031-949X</issn><eissn>1943-7684</eissn><coden>PHYTAJ</coden><abstract>Meta-analysis is the analysis of the results of multiple studies, which is typically performed in order to synthesize evidence from many possible sources in a formal probabilistic manner. 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subjects | accuracy Biological and medical sciences data analysis Data Interpretation, Statistical Fundamental and applied biological sciences. Psychology literature reviews meta-analysis Meta-Analysis as Topic methodology Models, Statistical Models, Theoretical Phytopathology. Animal pests. Plant and forest protection Plant Diseases - statistics & numerical data plant diseases and disorders plant pathology Plants - microbiology prediction Publication Bias Research Design risk assessment statistical analysis |
title | Meta-Analysis for Evidence Synthesis in Plant Pathology: An Overview |
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