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

Vaumourin E.; Vourc'h G.; Telfer S.; Lambin X.; Salih D.; Seitzer U.; Morand S.; Charbonnel N.; Vayssier-Taussat M.; Gasqui P.

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

2014

AGROVOC Keywords

anaplasma anaplasma phagocytophilum babesia babesia microti bartonella bovidae campagnol dynamique des populations grande-bretagne interaction logiciel maladie des animaux modèle mathématique monde méthode statistique parasitologie parasitose relation hôte pathogène rongeur soudan theileria theileria mutans theileria parva transmission des maladies épidémiologie étude de cas

Bibliographic information

Published in

Frontiers in Cellular and Infection Microbiology

Volume 4 Issue 62

Pagination

11 p.

Other Subjects

Theileria velifera

Type

Journal Article; Journal Part

Source

Vaumourin 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, 4 (62) : 11 p. http://dx.doi.org/10.3389/fcimb.2014.00062

In AGRIS since: 2017-07-15

Format: AGRIS AP

Data Provider

This bibliographic record has been provided by Centre de coopération internationale en recherche agronomique pour le développement

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Links

DOI http://publications.cirad.fr/une_notice.php?dk=575441

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