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To be or not to be associated : power study of four statistical modeling approaches to identify parasite associations in cross-sectional studies

dc.contributor.authorVaumourin, Elise
dc.contributor.authorVourc'h, Gwenael
dc.contributor.authorTelfer, Sandra
dc.contributor.authorLambin, Xavier
dc.contributor.authorSalih, Diaeldin
dc.contributor.authorSeitzer, Ulrike
dc.contributor.authorMorand, Serge
dc.contributor.authorCharbonnel, Nathalie
dc.contributor.authorVayssier-Taussat, Muriel
dc.contributor.authorGasqui, Patrick
dc.date.accessioned2015-01-27T10:33:01Z
dc.date.available2015-01-27T10:33:01Z
dc.date.issued2014-05-15
dc.identifier45920909
dc.identifier3376d27c-4b54-4caf-b59f-eb940b5d9a06
dc.identifier24860791
dc.identifier000338986800009
dc.identifier84988038506
dc.identifier.citationVaumourin , E , Vourc'h , G , Telfer , S , Lambin , X , Salih , D , Seitzer , U , Morand , S , Charbonnel , N , Vayssier-Taussat , M & Gasqui , P 2014 , ' To be or not to be associated : power study of four statistical modeling approaches to identify parasite associations in cross-sectional studies ' , Frontiers in cellular and infection microbiology , vol. 4 , 62 . https://doi.org/10.3389/fcimb.2014.00062en
dc.identifier.issn2235-2988
dc.identifier.otherORCID: /0000-0003-4643-2653/work/95222447
dc.identifier.urihttp://hdl.handle.net/2164/4206
dc.descriptionAcknowledgments We are grateful to the « Tiques et Maladies à Tiques » working group of the « Réseau Ecologie des Interactions Durables » for discussion and support. This modeling work was supported by the Animal Health department of National Institute of Agronomic Research (http://www.inra.fr), Auvergne region (http://www.auvergnesciences.com), the Metaprogramme MEM (projet Patho-ID) of INRA and the EU grant FP7-261504 EDENext. It is cataloged by the EDENext Steering Committee as EDENext208 (http://www.edenext.eu). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. The field vole fieldwork was supported by funding from the Natural Environment Research Council (grant GR3/13051) and the Wellcome Trust (grants 075202/Z/04/Z and 070675/Z/03/Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.format.extent11
dc.format.extent1568360
dc.language.isoeng
dc.relation.ispartofFrontiers in cellular and infection microbiologyen
dc.subjectassociationsen
dc.subjectinteractionsen
dc.subjectmodelingen
dc.subjectparasite communityen
dc.subjectscreeningen
dc.subjectGLM approachen
dc.subjectnetwork modelen
dc.subjectchi-square testen
dc.subjectcomponent community structureen
dc.subjectcentral equatoria stateen
dc.subjectLake District Regionen
dc.subjectmolecular-detectionen
dc.subjectSouthern Sudanen
dc.subjectNorthern Spainen
dc.subjectnetworksen
dc.subjectBabesiaen
dc.subjectpopulationen
dc.subjectTheileriaen
dc.subjectQR Microbiologyen
dc.subject.lccQRen
dc.titleTo be or not to be associated : power study of four statistical modeling approaches to identify parasite associations in cross-sectional studiesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Biological Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Environment and Food Securityen
dc.description.statusPeer revieweden
dc.identifier.doi10.3389/fcimb.2014.00062


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