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

Volumn 4, Issue MAY, 2014, Pages

  Vaumourin, Elise ^a,b   Vourc'h, Gwenaël ^a   Telfer, Sandra ^c   Lambin, Xavier ^c   Salih, Diaeldin ^d   Seitzer, Ulrike ^e   Morand, Serge ^f,g   Charbonnel, Nathalie ^h   Vayssier Taussat, Muriel ^b   Gasqui, Patrick ^a  

^a EPIA   (France)

^b INRA   (France)

^c UNIVERSITY OF ABERDEEN   (United Kingdom)

^d Veterinary Research Institute   (Sudan)

^e RESEARCH CENTER BORSTEL   (Germany)

^f INSTITUT DES SCIENCES DE L EVOLUTION   (France)

^g CIRAD   (France)

^h CENTRE DE BIOLOGIE ET DE GESTION DES POPULATIONS   (France)

Author keywords

Associations; Chi square test; GLM approach; Interactions; Modeling; Network model; Parasite community; Screening

Indexed keywords

ANAPLASMA PHAGOCYTOPHILUM; BABESIA MICROTI; BARTONELLA; BOVINE; CHI SQUARE TEST; CROSS-SECTIONAL STUDY; DIFFERENTIATION; GENOMICS; HOST PARASITE INTERACTION; NONHUMAN; RODENT; SCREENING; STATISTICAL MODEL; THEILERIA; VELOCITY; ALGORITHM; ANIMAL; COMPUTER SIMULATION; HUMAN; MIXED INFECTION; PARASITE; PARASITOLOGY; PARASITOSIS;

ALGORITHMS; ANIMALS; COINFECTION; COMPUTER SIMULATION; CROSS-SECTIONAL STUDIES; HUMANS; MODELS, STATISTICAL; PARASITES; PARASITIC DISEASES;

EID: 84988038506     PISSN: None     EISSN: 22352988     Source Type: Journal    
DOI: 10.3389/fcimb.2014.00062     Document Type: Article

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